24.1.8 Time Series Analysis, One-D Waveform Analysis, One-D Signals

Love, P.L., Simaan, M.,
Automatic recognition of primitive changes in manufacturing process signals,
PR(21), No. 4, 1988, pp. 333-342.
Elsevier DOI 0309
BibRef

Leskow, J.[Jacek], Napolitano, A.[Antonio],
Quantile prediction for time series in the fraction-of-time probability framework,
SP(82), No. 11, November 2002, pp. 1727-1741.
Elsevier DOI 0210
BibRef

Saadani, A., Gelpi, P., Tortelier, P.,
A variable-order Markov-chain-based model for Rayleigh fading and RAKE receiver,
SPLetters(11), No. 3, March 2004, pp. 356-358.
IEEE Abstract. 0404
BibRef

Li, Y., Vucetic, B., Tang, Y., Zhang, Q.,
Space-Time Trellis Codes With Linear Transformation for Fast Fading Channels,
SPLetters(11), No. 11, November 2004, pp. 895-898.
IEEE Abstract. 0411
BibRef

Liao, T.W.[T. Warren],
Clustering of time series data: A survey,
PR(38), No. 11, November 2005, pp. 1857-1874.
Elsevier DOI Award, Pattern Recognition, Honorable Mention. 0509
BibRef

Liao, T.W.[T. Warren],
A clustering procedure for exploratory mining of vector time series,
PR(40), No. 9, September 2007, pp. 2550-2562.
Elsevier DOI 0705
Vector time series; Clustering; Clustering algorithms; Validity index BibRef

Wu, J.K., Liang, Y., Wu, Q., Chen, G.T.,
Frequency tracking techniques of power systems in coloured noises,
VISP(153), No. 6, December 2006, pp. 795-804.
DOI Link 0702
BibRef

Sarkar, M.[Manish],
Ruggedness measures of medical time series using fuzzy-rough sets and fractals,
PRL(27), No. 5, 1 April 2006, pp. 447-454.
Elsevier DOI Characterization; Time series; Fuzzy; Rough; Fuzzy-rough; Hurst exponent and fractal 0604
BibRef

Rajagopalan, V.[Venkatesh], Ray, A.[Asok], Samsi, R.[Rohan], Mayer, J.[Jeffrey],
Pattern identification in dynamical systems via symbolic time series analysis,
PR(40), No. 11, November 2007, pp. 2897-2907.
Elsevier DOI 0707
Time series patterns. Pattern classification; Symbolic time series analysis; Markov modeling BibRef

Yan, K., Jiang, J., Wang, Y.G., Liu, H.T.,
Outage Probability of Selection Cooperation With MRC in Nakagami-m Fading Channels,
SPLetters(16), No. 12, December 2009, pp. 1031-1034.
IEEE DOI 0909
BibRef

Cami, A.[Aurel], Wallstrom, G.L.[Garrick L.], Fowlkes, A.L.[Ashley L.], Panozzo, C.A.[Cathy A.], Hogan, W.R.[William R.],
Mining aggregates of over-the-counter products for syndromic surveillance,
PRL(30), No. 3, 1 February 2009, pp. 255-266.
Elsevier DOI 0804
Biosurveillance; Outbreak detection; Linear regression; Time series aggregation BibRef

Kowalski, M.[Matthieu], Torrésani, B.[Bruno],
Sparsity and persistence: mixed norms provide simple signal models with dependent coefficients,
SIViP(3), No. 3, September 2009, pp. xx-yy.
Springer DOI 0910
BibRef

Thomas, T., Weijermars, W., van Berkum, E.,
Predictions of Urban Volumes in Single Time Series,
ITS(11), No. 1, March 2010, pp. 71-80.
IEEE DOI 1003
BibRef

Orsenigo, C., Vercellis, C.,
Combining discrete SVM and fixed cardinality warping distances for multivariate time series classification,
PR(43), No. 11, November 2010, pp. 3787-3794.
Elsevier DOI 1008
Time series classification; Support vector machines; Discrete support vector machines; Learning theory; Warping distance BibRef

Jin, X.[Xin], Gupta, S.[Shalabh], Mukherjee, K.[Kushal], Ray, A.[Asok],
Wavelet-based feature extraction using probabilistic finite state automata for pattern classification,
PR(44), No. 7, July 2011, pp. 1343-1356.
Elsevier DOI 1103
Time series analysis; Symbolic dynamics; Feature extraction; Pattern classification; Probabilistic finite state automata BibRef

Yang, C., Le Bouquin Jeannes, R., Faucon, G., Shu, H.,
Extracting Information on Flow Direction in Multivariate Time Series,
SPLetters(18), No. 4, April 2011, pp. 251-254.
IEEE DOI 1103
BibRef

Yang, H.[Hui], Bukkapatnam, S.T.S.[Satish T.S.], Barajas, L.G.[Leandro G.],
Local recurrence based performance prediction and prognostics in the nonlinear and nonstationary systems,
PR(44), No. 8, August 2011, pp. 1834-1840.
Elsevier DOI 1104
Prediction; Recurrence plot; Nonstationary; Time series BibRef

Jeong, Y.S.[Young-Seon], Jeong, M.K.[Myong K.], Omitaomu, O.A.[Olufemi A.],
Weighted dynamic time warping for time series classification,
PR(44), No. 9, September 2011, pp. 2231-2240.
Elsevier DOI 1106
Dynamic time warping; Adaptive weights; Weighted dynamic time warping; Modified logistic weight function; Time series classification; Time series clustering BibRef

Fidalgo-Merino, R., Nunez, M.,
Self-Adaptive Induction of Regression Trees,
PAMI(33), No. 8, August 2011, pp. 1659-1672.
IEEE DOI 1107
Binary regression tree dealing with data stream. BibRef

Oya, A., Navarro-Moreno, J., Ruiz-Molina, J.C.,
Widely Linear Simulation of Continuous-Time Complex-Valued Random Signals,
SPLetters(18), No. 9, September 2011, pp. 513-516.
IEEE DOI 1108
BibRef

Navarro-Moreno, J., Fernandez-Alcala, R.M., Ruiz-Molina, J.C.,
A Quaternion Widely Linear Series Expansion and Its Applications,
SPLetters(19), No. 12, December 2012, pp. 868-871.
IEEE DOI 1212
BibRef

Martinez-Alvarez, F., Troncoso, A., Riquelme, J.C., Aguilar-Ruiz, J.S.,
Discovery of motifs to forecast outlier occurrence in time series,
PRL(32), No. 12, 1 September 2011, pp. 1652-1665.
Elsevier DOI 1108
Time series forecasting; Pattern recognition; Motifs; Outliers BibRef

Sayed-Mouchaweh, M.[Moamar], Messai, N.[Nadhir],
A clustering-based approach for the identification of a class of temporally switched linear systems,
PRL(33), No. 2, 15 January 2012, pp. 144-151.
Elsevier DOI 1112
Classification; Clustering; Identification; Hybrid dynamic systems BibRef

Martin, R.K.,
Using Alpha Shapes to Approximate Signal Strength Based Positioning Performance,
SPLetters(18), No. 12, December 2011, pp. 741-744.
IEEE DOI 1112
BibRef

Li, R.[Rui], Tian, T.P.[Tai-Peng], Sclaroff, S.[Stan],
Divide, Conquer and Coordinate: Globally Coordinated Switching Linear Dynamical System,
PAMI(34), No. 4, April 2012, pp. 654-669.
IEEE DOI 1203
Representation for high-dimensional time series. BibRef

Ward, J.P., Kirshner, H., Unser, M.,
Is Uniqueness Lost for Under-Sampled Continuous-Time Auto-Regressive Processes?,
SPLetters(19), No. 4, April 2012, pp. 183-186.
IEEE DOI 1203
BibRef

Zhang, L.[Lin], Luo, Z.[Zhen], Leung, S.H.[Shu-Hung], Zhu, Y.S.[Yue-Sheng],
Simplified Precoder Design for MIMO Systems With Receive Correlation in Ricean Channels,
SPLetters(19), No. 5, May 2012, pp. 263-266.
IEEE DOI 1204
BibRef

Sugavaneswaran, L., Xie, S., Umapathy, K., Krishnan, S.,
Time-Frequency Analysis via Ramanujan Sums,
SPLetters(19), No. 6, June 2012, pp. 352-355.
IEEE DOI 1202
BibRef

Vallejos, R.O.[Ronny O.],
Testing for the absence of correlation between two spatial or temporal sequences,
PRL(33), No. 13, 1 October 2012, pp. 1741-1748.
Elsevier DOI 1208
Codispersion coefficient; Time series; Spatial processes; Hypothesis testing BibRef

Valério, D.[Duarte], da Costa, J.S.[José Sá],
Fractional reset control,
SIViP(6), No. 3, September 2012, pp. 495-501.
WWW Link. 1209
BibRef

Chen, D.[Dali], Chen, Y.Q.[Yang-Quan], Xue, D.Y.[Ding-Yu],
1-D and 2-D digital fractional-order Savitzky-Golay differentiator,
SIViP(6), No. 3, September 2012, pp. 503-511.
WWW Link. 1209
BibRef

Bhalekar, S.[Sachin],
Dynamical analysis of fractional order Uçar prototype delayed system,
SIViP(6), No. 3, September 2012, pp. 513-519.
WWW Link. 1209
BibRef

Bhalekar, S.[Sachin],
On the Uçar prototype model with incommensurate delays,
SIViP(8), No. 4, May 2014, pp. 635-639.
WWW Link. 1404
BibRef

Bhalekar, S.[Sachin],
Stability analysis of Uçar prototype delayed system,
SIViP(10), No. 4, April 2016, pp. 777-781.
WWW Link. 1604
BibRef

Dulf, E.H.[Eva-Henrietta], Pop, C.I.[Cristina-Ioana], Dulf, F.V.[Francisc-Vasile],
Fractional calculus in 13C separation column control,
SIViP(6), No. 3, September 2012, pp. 479-485.
WWW Link. 1209
BibRef

Pop, C.I.[Cristina I.], Ionescu, C.M.[Clara M.], de Keyser, R.[Robain], Dulf, E.H.[Eva H.],
Robustness evaluation of fractional order control for varying time delay processes,
SIViP(6), No. 3, September 2012, pp. 453-461.
WWW Link. 1209
BibRef

Hosseinnia, S.H.[S. Hassan], Tejado, I.[Inés], Vinagre, B.M.[Blas M.], Sierociuk, D.[Dominik],
Boolean-based fractional order SMC for switching systems: Application to a DC-DC buck converter,
SIViP(6), No. 3, September 2012, pp. 445-451.
WWW Link. 1209
BibRef

Melchior, P.[Pierre], Pellet, M.[Mathieu], Petit, J.[Julien], Cabelguen, J.M.[Jean-Marie], Oustaloup, A.[Alain],
Analysis of muscle length effect on an S type motor-unit fractional multi-model,
SIViP(6), No. 3, September 2012, pp. 421-428.
WWW Link. 1209
BibRef

Bensouici, T.[Tahar], Charef, A.[Abdelfatah],
Approximate realization of digital fractional forward operator using digital IIR filter,
SIViP(6), No. 3, September 2012, pp. 411-420.
WWW Link. 1209
BibRef

Maione, G.[Guido],
Thiele's continued fractions in digital implementation of noninteger differintegrators,
SIViP(6), No. 3, September 2012, pp. 401-410.
WWW Link. 1209
BibRef

Malti, R.[Rachid], Melchior, P.[Pierre], Lanusse, P.[Patrick], Oustaloup, A.[Alain],
Object-oriented CRONE toolbox for fractional differential signal processing,
SIViP(6), No. 3, September 2012, pp. 393-400.
WWW Link. 1209
BibRef

Adams, J.L.[Jay L.], Veillette, R.J.[Robert J.], Hartley, T.T.[Tom T.],
Conjugate-order systems for signal processing: Stability, causality, boundedness, compactness,
SIViP(6), No. 3, September 2012, pp. 373-380.
WWW Link. 1209
BibRef

Couceiro, M.S.[Micael S.], Rocha, R.P.[Rui P.], Ferreira, N.M.F.[N. M. Fonseca], Machado, J.A.T.[J. A. Tenreiro],
Introducing the fractional-order Darwinian PSO,
SIViP(6), No. 3, September 2012, pp. 343-350.
WWW Link. 1209
BibRef

Ortigueira, M.D.[Manuel D.], Magin, R.L.[Richard L.], Trujillo, J.J.[Juan J.], Velasco, M.P.[M. Pilar],
A real regularised fractional derivative,
SIViP(6), No. 3, September 2012, pp. 351-358.
WWW Link. 1209
BibRef

Trigeassou, J.C., Maamri, N., Sabatier, J., Oustaloup, A.,
Transients of fractional-order integrator and derivatives,
SIViP(6), No. 3, September 2012, pp. 359-372.
WWW Link. 1209
BibRef

Venkitaraman, A., Seelamantula, C.S.,
A Technique to Compute Smooth Amplitude, Phase, and Frequency Modulations From the Analytic Signal,
SPLetters(19), No. 10, October 2012, pp. 623-626.
IEEE DOI 1209
BibRef

Venkitaraman, A., Seelamantula, C.S.,
On Computing Amplitude, Phase, and Frequency Modulations Using a Vector Interpretation of the Analytic Signal,
SPLetters(20), No. 12, 2013, pp. 1187-1190.
IEEE DOI 1311
Eigenvalues and eigenfunctions BibRef

Venkitaraman, A., Seelamantula, C.S.,
Temporal Envelope Fit of Transient Audio Signals,
SPLetters(20), No. 12, 2013, pp. 1191-1194.
IEEE DOI 1311
Closed-form solutions BibRef

Onchis, D.M.[Darian M.],
Signal Reconstruction in Multi-Windows Spline-Spaces Using the Dual System,
SPLetters(19), No. 11, November 2012, pp. 729-732.
IEEE DOI 1210
BibRef

Lee, Y.[Yuni], Sung, Y.C.[Young-Chul],
Generalized Chernoff Information for Mismatched Bayesian Detection and Its Application to Energy Detection,
SPLetters(19), No. 11, November 2012, pp. 753-756.
IEEE DOI 1210
BibRef

Diouf, C., Telescu, M., Cloastre, P., Tanguy, N.,
On the Use of Equality Constraints in the Identification of Volterra-Laguerre Models,
SPLetters(19), No. 12, December 2012, pp. 857-860.
IEEE DOI 1212
BibRef

Sigg, C.D., Dikk, T., Buhmann, J.M.,
Learning Dictionaries With Bounded Self-Coherence,
SPLetters(19), No. 12, December 2012, pp. 861-864.
IEEE DOI 1212
BibRef

Hou, Y., Liu, G., Wang, Q., Xiang, W.,
Performance Optimization of Digital Spectrum Analyzer With Gaussian Input Signal,
SPLetters(20), No. 1, January 2013, pp. 31-34.
IEEE DOI 1212
BibRef

Mohammadi, M.[Mokhtar], Pouyan, A.A.[Ali Akbar], Khan, N.A.[Nabeel Ali],
A highly adaptive directional time-frequency distribution,
SIViP(10), No. 7, October 2016, pp. 1369-1376.
WWW Link. 1609
BibRef

Wu, G.[Gang], Yang, J.W.[Ji-Wen],
A representation of time series based on implicit polynomial curve,
PRL(34), No. 4, 1 March 2013, pp. 361-371.
Elsevier DOI 1302
Implicit polynomial curve; Time series; Similarity measure; Dimension reduction BibRef

Fang, J.[Jun], Liu, Y.M.[Yu-Meng], Li, H.B.[Hong-Bin], Li, S.Q.[Shao-Qian],
One-Bit Quantizer Design for Multisensor GLRT Fusion,
SPLetters(20), No. 3, March 2013, pp. 257-260.
IEEE DOI 1303
BibRef

Rajashekar, R., Hari, K.V.S., Hanzo, L.,
Structured dispersion matrices from division algebra codes for space-time shift keying,
SPLetters(20), No. 4, April 2013, pp. 371-374.
IEEE DOI 1303
BibRef

Rajashekar, R., Hari, K.V.S., Hanzo, L.,
A Reduced-Complexity Partial-Interference-Cancellation Group Decoder for STBCs,
SPLetters(20), No. 10, 2013, pp. 929-932.
IEEE DOI 1309
Toeplitz matrices BibRef

Setlur, P., Devroye, N.,
An Information Theoretic Take on Time Reversal for Nonstationary Channels,
SPLetters(20), No. 4, April 2013, pp. 327-330.
IEEE DOI 1303
BibRef

Xing, C., Wang, N., Ni, J., Fei, Z., Kuang, J.,
MIMO Beamforming Designs With Partial CSI Under Energy Harvesting Constraints,
SPLetters(20), No. 4, April 2013, pp. 363-366.
IEEE DOI 1303
BibRef

Li, P.[Po], Wang, D.C.[De-Chun], Wang, L.[Lu],
Separation of micro-Doppler signals based on time frequency filter and Viterbi algorithm,
SIViP(7), No. 3, May 2013, pp. 593-605.
WWW Link. 1305
BibRef

Vemulapalli, P.K.[Pramod K.], Monga, V.[Vishal], Brennan, S.N.[Sean N.],
Robust Extrema Features for Time-Series Data Analysis,
PAMI(35), No. 6, June 2013, pp. 1464-1479.
IEEE DOI 1305
BibRef

Ferreira, A.L., Piccinini, G.F.B.[G.F. Bianchi], Rola, S., Simoes, A.,
Gender and age-related differences in the perception of in-vehicle mobile phone usage among Portuguese drivers,
IET-ITS(7), No. 2, 2013, pp. -.
DOI Link 1307
BibRef

Hancox, G., Richardson, J., Morris, A.,
Drivers' willingness to engage with their mobile phone: The influence of phone function and road demand,
IET-ITS(7), No. 2, 2013, pp. xx-yy.
DOI Link 1307
BibRef

Hayashi, A.[Akira], Iwata, K.[Kazunori], Suematsu, N.[Nobuo],
Marginalized Viterbi algorithm for hierarchical hidden Markov models,
PR(46), No. 12, 2013, pp. 3452-3459.
Elsevier DOI 1308
Time series data BibRef

Montillet, J.P., McClusky, S., Yu, K.[Kegen],
Extracting Colored Noise Statistics in Time Series via Negentropy,
SPLetters(20), No. 9, 2013, pp. 857-860.
IEEE DOI 1308
convex programming BibRef

Diversi, R.[Roberto], Guidorzi, R.[Roberto],
Optimal filtering of multivariate noisy AR processes,
SIViP(7), No. 5, September 2013, pp. 873-878.
Springer DOI 1309
BibRef

Baydogan, M.G.[Mustafa Gokce], Runger, G.[George], Tuv, E.[Eugene],
A Bag-of-Features Framework to Classify Time Series,
PAMI(35), No. 11, 2013, pp. 2796-2802.
IEEE DOI 1309
Supervised learning; codebook; feature extraction BibRef

Wang, J., Li, X., Liao, S.S., Hua, Z.,
A Hybrid Approach for Automatic Incident Detection,
ITS(14), No. 3, 2013, pp. 1176-1185.
IEEE DOI 1309
Automatic incident detection (AID) BibRef

Xu, G.L.[Guan-Lei], Wang, X.T.[Xiao-Tong], Wang, L.T.[Long-Tao], Liu, B.[Bo], Su, S.P.[Shi-Peng], Xu, X.G.[Xiao-Gang],
Generalized uncertainty principles associated with Hilbert transform,
SIViP(8), No. 2, February 2014, pp. 279-285.
Springer DOI 1402
BibRef

Çelik, V.[Vedat], Demir, Y.[Yakup],
Chaotic dynamics of the fractional order nonlinear system with time delay,
SIViP(8), No. 1, January 2014, pp. 65-70.
WWW Link. 1402
BibRef

Tan, V.Y.F., Atia, G.K.,
Strong Impossibility Results for Sparse Signal Processing,
SPLetters(21), No. 3, March 2014, pp. 260-264.
IEEE DOI 1403
error statistics BibRef

Kazemi, K.[Kamran], Amirian, M.[Mohammadreza], Dehghani, M.J.[Mohammad Javad],
The S-transform using a new window to improve frequency and time resolutions,
SIViP(8), No. 3, March 2014, pp. 533-541.
Springer DOI 1403
BibRef

Liu, Z.L.[Zi-Long], Parampalli, U., Guan, Y.L.[Yong Liang],
On Even-Period Binary Z-Complementary Pairs with Large ZCZs,
SPLetters(21), No. 3, March 2014, pp. 284-287.
IEEE DOI 1403
Golay codes BibRef

Adhikary, A.R.[Avik Ranjan], Majhi, S.[Sudhan], Liu, Z.L.[Zi-Long], Guan, Y.L.[Yong Liang],
New Sets of Even-Length Binary Z-Complementary Pairs With Asymptotic ZCZ Ratio of 3/4,
SPLetters(25), No. 7, July 2018, pp. 970-973.
IEEE DOI 1807
Golay codes, binary codes, correlation methods, sequences, ZCZ region, ZCZ width, asymptotic ZCZ ratio, zero correlation zone (ZCZ) BibRef

López-Yáñez, I.[Itzamá], Sheremetov, L.[Leonid], Yáñez-Márquez, C.[Cornelio],
A novel associative model for time series data mining,
PRL(41), No. 1, 2014, pp. 23-33.
Elsevier DOI 1403
Time series data mining BibRef

Längkvist, M.[Martin], Karlsson, L.[Lars], Loutfi, A.[Amy],
A review of unsupervised feature learning and deep learning for time-series modeling,
PRL(42), No. 1, 2014, pp. 11-24.
Elsevier DOI 1404
Time-series BibRef

Górecki, T.[Tomasz],
Using derivatives in a longest common subsequence dissimilarity measure for time series classification,
PRL(45), No. 1, 2014, pp. 99-105.
Elsevier DOI 1407
Longest common subsequence BibRef

Boecking, B.[Benedikt], Chalup, S.K.[Stephan K.], Seese, D.[Detlef], Wong, A.S.W.[Aaron S.W.],
Support vector clustering of time series data with alignment kernels,
PRL(45), No. 1, 2014, pp. 129-135.
Elsevier DOI 1407
Support vector clustering BibRef

Zhao, H.[Hui], Wang, R.[Ruyan], Song, D.P.[Dai-Ping], Wu, D.P.[Da-Peng],
Maximally concentrated sequences in both time and linear canonical transform domains,
SIViP(8), No. 5, July 2014, pp. 819-829.
Springer DOI 1407
BibRef

Frasca, M.[Marco], Farina, A.[Alfonso],
Tartaglia-Pascal triangle and Brownian motion in non-euclidean geometries: application to heat and Black-Scholes equations,
SIViP(8), No. 6, September 2014, pp. 1149-1157.
Springer DOI 1408
Financial models. BibRef

Liu, X.[Xiang], Kosakowski, M.,
Max-Log-MAP Soft Demapper with Logarithmic Complexity for M-PAM Signals,
SPLetters(22), No. 1, January 2015, pp. 50-53.
IEEE DOI 1410
computational complexity quadrature amplitude modulation. BibRef

Naraghi-Pour, M., Soltanmohammadi, E.,
Tenor: A Measure of Central Tendency for Distributed Networks,
SPLetters(22), No. 1, January 2015, pp. 58-61.
IEEE DOI 1410
Phase of the first non-zero frequency of the discrete Fourier transform of the pmf. BibRef

Garcia-Trevino, E.S., Barria, J.A.,
Structural Generative Descriptions for Time Series Classification,
Cyber(44), No. 10, October 2014, pp. 1978-1991.
IEEE DOI 1410
data mining BibRef

Wang, Z.X.[Zhi-Xin], Chan, C.F.[Cheung-Fat],
Continuous Function Modeling of Head-Related Impulse Response,
SPLetters(22), No. 3, March 2015, pp. 283-287.
IEEE DOI 1410
Azimuth BibRef

Maymon, S., Eldar, Y.C.,
The Viterbi Algorithm for Subset Selection,
SPLetters(22), No. 5, May 2015, pp. 524-528.
IEEE DOI 1411
Dictionaries BibRef

Gan, M.[Min], Chen, C.L.P., Li, H.X.[Han-Xiong], Chen, L.[Long],
Gradient Radial Basis Function Based Varying-Coefficient Autoregressive Model for Nonlinear and Nonstationary Time Series,
SPLetters(22), No. 7, July 2015, pp. 809-812.
IEEE DOI 1412
autoregressive processes BibRef

Gharehbaghi, A.[Arash], Ask, P.[Per], Babic, A.[Ankica],
A pattern recognition framework for detecting dynamic changes on cyclic time series,
PR(48), No. 3, 2015, pp. 696-708.
Elsevier DOI 1412
Hybrid model BibRef

Trigano, T., Barat, E., Dautremer, T., Montagu, T.,
Fast Digital Filtering of Spectrometric Data for Pile-up Correction,
SPLetters(22), No. 7, July 2015, pp. 973-977.
IEEE DOI 1412
Detectors BibRef

Trigano, T., Gildin, I., Sepulcre, Y.,
Pileup Correction Algorithm using an Iterated Sparse Reconstruction Method,
SPLetters(22), No. 9, September 2015, pp. 1392-1395.
IEEE DOI 1503
Computational modeling BibRef

Hensman, J., Rattray, M., Lawrence, N.D.,
Fast Nonparametric Clustering of Structured Time-Series,
PAMI(37), No. 2, February 2015, pp. 383-393.
IEEE DOI 1502
Biological system modeling BibRef

Xu, Z., MacEachern, S., Xu, X.,
Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model,
PAMI(37), No. 2, February 2015, pp. 372-382.
IEEE DOI 1502
Analytical models BibRef

Kumar, M., Stoll, N., Stoll, R., Thurow, K.,
A Stochastic Framework for Robust Fuzzy Filtering and Analysis of Signals: Part I,
Cyber(46), No. 5, May 2016, pp. 1118-1131.
IEEE DOI 1604
Algorithm design and analysis BibRef

Kumar, M., Stoll, N., Stoll, R., Thurow, K.,
A Stochastic Framework for Robust Fuzzy Filtering and Analysis of Signals: Part II,
Cyber(45), No. 3, March 2015, pp. 486-496.
IEEE DOI 1502
Algorithm design and analysis BibRef

Naha, A., Samanta, A.K., Routray, A., Deb, A.K.,
Determining Autocorrelation Matrix Size and Sampling Frequency for MUSIC Algorithm,
SPLetters(22), No. 8, August 2015, pp. 1016-1020.
IEEE DOI 1502
correlation methods BibRef

Pei, S.C.[Soo-Chang], Chang, K.W.[Kuo-Wei],
Perfect Gaussian Integer Sequences of Arbitrary Length,
SPLetters(22), No. 8, August 2015, pp. 1040-1044.
IEEE DOI 1502
Gaussian processes BibRef

Markovsky, I.,
Comparison of Adaptive and Model-Free Methods for Dynamic Measurement,
SPLetters(22), No. 8, August 2015, pp. 1094-1097.
IEEE DOI 1502
adaptive signal processing BibRef

Pei, S.C.[Soo-Chang], Lu, K.S.[Keng-Shih],
Intrinsic Integer-Periodic Functions for Discrete Periodicity Detection,
SPLetters(22), No. 8, August 2015, pp. 1108-1112.
IEEE DOI 1502
signal sampling BibRef

Jung, A., Hannak, G., Goertz, N.,
Graphical LASSO based Model Selection for Time Series,
SPLetters(22), No. 10, October 2015, pp. 1781-1785.
IEEE DOI 1506
Algorithm design and analysis BibRef

Walker, J.S.[James S.], Jones, M.W.[Mark W.], Laramee, R.S.[Robert S.], Bidder, O.R.[Owen R.], Williams, H.J.[Hannah J.], Scott, R.[Rebecca], Shepard, E.L.C.[Emily L. C.], Wilson, R.P.[Rory P.],
TimeClassifier: a visual analytic system for the classification of multi-dimensional time series data,
VC(31), No. 6-8, June 2015, pp. 1067-1078.
Springer DOI 1506
BibRef

Farokhi, F., Shames, I., Cantoni, M.,
Promoting Truthful Behavior in Participatory-Sensing Mechanisms,
SPLetters(22), No. 10, October 2015, pp. 1538-1542.
IEEE DOI 1506
game theory BibRef

Konar, A., Sidiropoulos, N.D.,
Hidden Convexity in QCQP with Toeplitz-Hermitian Quadratics,
SPLetters(22), No. 10, October 2015, pp. 1623-1627.
IEEE DOI 1506
Hermitian matrices. Quadratically Constrained Quadratic Programming. BibRef

Wei, X.Y.[Xiao-Yao], Dragotti, P.L.[Pier Luigi],
Guaranteed Performance in the FRI Setting,
SPLetters(22), No. 10, October 2015, pp. 1661-1665.
IEEE DOI 1506
noise Finite Rate of Innovation. BibRef

Tenneti, S.V., Vaidyanathan, P.P.,
Arbitrarily Shaped Periods in Multidimensional Discrete Time Periodicity,
SPLetters(22), No. 10, October 2015, pp. 1748-1751.
IEEE DOI 1506
mathematical analysis BibRef

Elvira, V., Martino, L., Luengo, D., Bugallo, M.F.,
Heretical Multiple Importance Sampling,
SPLetters(23), No. 10, October 2016, pp. 1474-1478.
IEEE DOI 1610
importance sampling BibRef

Yang, Y.L.[Yan-Li], Deng, J.H.[Jia-Hao], Kang, D.[Dali],
An improved empirical mode decomposition by using dyadic masking signals,
SIViP(9), No. 6, September 2015, pp. 1259-1263.
Springer DOI 1509
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Khan, M.[Majid], Shah, T.[Tariq],
An efficient construction of substitution box with fractional chaotic system,
SIViP(9), No. 6, September 2015, pp. 1335-1338.
Springer DOI 1509
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Luo, W.[Wen], Yu, Z.Y.[Zhao-Yuan], Xiao, S.J.[Sheng-Jun], Zhu, A.X.[A-Xing], Yuan, L.W.[Lin-Wang],
Exploratory Method for Spatio-Temporal Feature Extraction and Clustering: An Integrated Multi-Scale Framework,
IJGI(4), No. 4, 2015, pp. 1870.
DOI Link 1511
E.g. weather data and El Nino. BibRef

Biswas, N., Ray, P., Varshney, P.K.,
Distributed Detection Over Channels with Memory,
SPLetters(22), No. 12, December 2015, pp. 2494-2498.
IEEE DOI 1512
Markov processes BibRef

Khanduri, P., Kailkhura, B., Thiagarajan, J.J., Varshney, P.K.,
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach,
SPLetters(23), No. 10, October 2016, pp. 1484-1488.
IEEE DOI 1610
compressed sensing BibRef

Ren, H.R.[Huo-Rong], Ren, A.[An], Li, Z.[Zhiwu],
A new strategy for the suppression of cross-terms in pseudo Wigner-Ville distribution,
SIViP(10), No. 1, January 2016, pp. 139-144.
Springer DOI 1601
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Rostaghi, M., Azami, H.,
Dispersion Entropy: A Measure for Time-Series Analysis,
SPLetters(23), No. 5, May 2016, pp. 610-614.
IEEE DOI 1604
Bandwidth BibRef

Mohammadi, H., Steele, C., Chau, T.,
Post-Segmentation Swallowing Accelerometry Signal Trimming and False Positive Reduction,
SPLetters(23), No. 9, September 2016, pp. 1221-1225.
IEEE DOI 1609
accelerometers BibRef

Yuan, W.J.[Wei-Jie], Wu, N.[Nan], Wang, H.[Hua], Kuang, J.M.[Jing-Ming],
Variational Inference-Based Frequency-Domain Equalization for Faster-Than-Nyquist Signaling in Doubly Selective Channels,
SPLetters(23), No. 9, September 2016, pp. 1270-1274.
IEEE DOI 1609
computational complexity BibRef

Pirondini, E., Vybornova, A., Coscia, M., van de Ville, D.,
A Spectral Method for Generating Surrogate Graph Signals,
SPLetters(23), No. 9, September 2016, pp. 1275-1278.
IEEE DOI 1609
Fourier transforms BibRef

Shah, S.F.A.[S. Faisal A.], Wang, L.[Lei], Li, C.D.[Chuan-Dong], Zhang, Z.H.[Zhu-Hong],
Low-Complexity Design of Noninteger Fractionally Spaced Adaptive Equalizers for Coherent Optical Receivers,
SPLetters(23), No. 9, September 2016, pp. 1289-1293.
IEEE DOI 1609
adaptive equalisers BibRef

Sarkar, S.[Soumalya], Chattopdhyay, P.[Pritthi], Ray, A.[Asok],
Symbolization of dynamic data-driven systems for signal representation,
SIViP(10), No. 8, November 2016, pp. 1535-1542.
Springer DOI 1610
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Ferrand, P.,
Mi-Xing Oscillators for Phase Noise Reduction,
SPLetters(23), No. 11, November 2016, pp. 1597-1601.
IEEE DOI 1609
circuit noise BibRef

de Fréin, R., Rickard, S.T.,
Power-Weighted Divergences for Relative Attenuation and Delay Estimation,
SPLetters(23), No. 11, November 2016, pp. 1612-1616.
IEEE DOI 1609
approximation theory BibRef

Bayram, S., Dulek, B., Gezici, S.,
Joint Detection and Decoding in the Presence of Prior Information With Uncertainty,
SPLetters(23), No. 11, November 2016, pp. 1602-1606.
IEEE DOI 1609
decoding BibRef

Martínez-Rego, D.[David], Fontenla-Romero, O.[Oscar], Alonso-Betanzos, A.[Amparo], Principe, J.C.[José C.],
Fault detection via recurrence time statistics and one-class classification,
PRL(84), No. 1, 2016, pp. 8-14.
Elsevier DOI 1612
Vibration analysis BibRef

Asadi, N., Mirzaei, A., Haghshenas, E.,
Creating Discriminative Models for Time Series Classification and Clustering by HMM Ensembles,
Cyber(46), No. 12, December 2016, pp. 2899-2910.
IEEE DOI 1612
Biological system modeling BibRef

Johard, L.[Leonard], Ruffaldi, E.[Emanuele],
Self-organizing trajectories,
PRL(84), No. 1, 2016, pp. 177-184.
Elsevier DOI 1612
Shape averaging BibRef

Adhikary, A.R.[Avik Ranjan], Liu, Z.L.[Zi-Long], Guan, Y.L.[Yong Liang], Majhi, S.[Sudhan], Budishin, S.Z.[Srdjan Z.],
Optimal Binary Periodic Almost-Complementary Pairs,
SPLetters(23), No. 12, December 2016, pp. 1816-1820.
IEEE DOI 1612
binary sequences BibRef

Valsesia, D.[Diego], Magli, E.[Enrico],
Binary Adaptive Embeddings From Order Statistics of Random Projections,
SPLetters(24), No. 1, January 2017, pp. 111-115.
IEEE DOI 1702
signal processing BibRef

Oloo, F.[Francis], Wallentin, G.[Gudrun],
An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach,
IJGI(6), No. 1, 2017, pp. xx-yy.
DOI Link 1702
Simulate flight routes of homing pigeons. BibRef

Su, B.[Bing], Ding, X.Q.[Xiao-Qing], Liu, C.S.[Chang-Song], Wang, H.[Hao], Wu, Y.[Ying],
Discriminative Transformation for Multi-Dimensional Temporal Sequences,
IP(26), No. 7, July 2017, pp. 3579-3593.
IEEE DOI 1706
Adaptation models, Character recognition, Data models, Hidden Markov models, Training, Transforms, Max-min inter-sequence distance analysis, BibRef

Su, B.[Bing], Ding, X.Q.[Xiao-Qing], Wang, H.[Hao], Wu, Y.[Ying],
Discriminative Dimensionality Reduction for Multi-Dimensional Sequences,
PAMI(40), No. 1, January 2018, pp. 77-91.
IEEE DOI 1712
Analytical models, Computational modeling, Data models, Hidden Markov models, Sequences, sequence classification BibRef

Das, M.[Monidipa], Ghosh, S.K.[Soumya K.],
semBnet: A semantic Bayesian network for multivariate prediction of meteorological time series data,
PRL(93), No. 1, 2017, pp. 192-201.
Elsevier DOI 1706
Bayesian, network BibRef

Das, M.[Monidipa], Ghosh, S.K.[Soumya K.],
Data-driven approaches for meteorological time series prediction: A comparative study of the state-of-the-art computational intelligence techniques,
PRL(105), 2018, pp. 155-164.
Elsevier DOI 1804
Computational intelligence, Data-driven modeling, Bayesian network, Time series prediction, Meteorology BibRef

Ahmed, A.,
Compressive Acquisition and Least-Squares Reconstruction of Correlated Signals,
SPLetters(24), No. 7, July 2017, pp. 933-937.
IEEE DOI 1706
Coherence, Computer architecture, Fourier series, Frequency modulation, Signal reconstruction, Standards, Array processing, compressive sampling, correlated signals, micro-sensor arrays, randomized SVD, sub-Nyquist, sampling BibRef

Kasasbeh, H., Viswanathan, R., Cao, L.,
Noise Correlation Effect on Detection: Signals in Equicorrelated or Autoregressive(1) Gaussian,
SPLetters(24), No. 7, July 2017, pp. 1078-1082.
IEEE DOI 1706
Correlation, Covariance matrices, Eigenvalues and eigenfunctions, Gaussian noise, Sensors, Signal design, Signal to noise ratio, Autoregressive, Gaussian noise, correlation, multiple sensors, signal, detection BibRef

Audibert, L.[Lorenzo], Haddar, H.[Houssem],
The Generalized Linear Sampling Method for Limited Aperture Measurements,
SIIMS(10), No. 2, 2017, pp. 845-870.
DOI Link 1708
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Al Sarray, B.[Basad], Chrétien, S.[Stéphane], Clarkson, P.[Paul], Cottez, G.[Guillaume],
Enhancing Prony's method by nuclear norm penalization and extension to missing data,
SIViP(11), No. 6, September 2017, pp. 1089-1096.
Springer DOI 1708
modelling signals using a finite sum of exponential terms. Weather models, etc. BibRef

Dumitrescu, B.[Bogdan],
Designing Incoherent Frames With Only Matrix Vector Multiplications,
SPLetters(24), No. 9, September 2017, pp. 1265-1269.
IEEE DOI 1708
least squares approximations, minimax techniques, atom-by-atom optimization strategy, incoherent frames, min-max problem, mutual coherence, shifted power method, signal processing, Eigenvalues and eigenfunctions, Optimization, power method, weighted least squares. BibRef

de Mattos Neto, P.S.G.[Paulo S.G.], Cavalcanti, G.D.C.[George D.C.], Madeiro, F.[Francisco],
Nonlinear combination method of forecasters applied to PM time series,
PRL(95), No. 1, 2017, pp. 65-72.
Elsevier DOI 1708
Forecasting BibRef

Tuncel, K.S.[Kerem Sinan], Baydogan, M.G.[Mustafa Gokce],
Autoregressive forests for multivariate time series modeling,
PR(73), No. 1, 2018, pp. 202-215.
Elsevier DOI 1709
Multivariate time series BibRef

Dadkhahi, H., Duarte, M.F., Marlin, B.M.,
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series,
IP(26), No. 11, November 2017, pp. 5435-5446.
IEEE DOI 1709
Data models, Manifolds, Noise measurement, Principal component analysis, Sensors, Time series analysis, Training, dimensionality reduction, out-of-sample extension. BibRef

Sharabiani, A., Darabi, H., Rezaei, A., Harford, S., Johnson, H., Karim, F.,
Efficient Classification of Long Time Series by 3-D Dynamic Time Warping,
SMCS(47), No. 10, October 2017, pp. 2688-2703.
IEEE DOI 1709
Cybernetics, Heuristic algorithms, Robot sensing systems, Robustness, Time measurement, Time series analysis, Training, Approximation methods, dynamic time warping (DTW), time, series, classification, (TSC) BibRef

Darsena, D., Gelli, G., Iudice, I., Verde, F.,
Second-Order Statistics of One-Sided CPM Signals,
SPLetters(24), No. 10, October 2017, pp. 1512-1516.
IEEE DOI 1710
CPM: continuous phase modulation, correlation methods, higher order statistics, signal representation, BibRef

Morel, M.[Marion], Achard, C.[Catherine], Kulpa, R.[Richard], Dubuisson, S.[Séverine],
Time-series averaging using constrained dynamic time warping with tolerance,
PR(74), No. 1, 2018, pp. 77-89.
Elsevier DOI 1711
Time-series averaging BibRef

Schultz, D.[David], Jain, B.J.[Brijnesh J.],
Nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces,
PR(74), No. 1, 2018, pp. 340-358.
Elsevier DOI 1711
Dynamic time warping BibRef

Jain, B.J.[Brijnesh J.], Schultz, D.[David],
Asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces,
PR(76), No. 1, 2018, pp. 349-366.
Elsevier DOI 1801
Learning vector quantization BibRef

Jain, B.J.[Brijnesh J.],
Making the dynamic time warping distance warping-invariant,
PR(94), 2019, pp. 35-52.
Elsevier DOI 1906
Time series, Dynamic time warping, Semi-metric, Nearest-neighbor rule BibRef

Mikalsen, K.Ø.[Karl Øyvind], Bianchi, F.M.[Filippo Maria], Soguero-Ruiz, C.[Cristina], Jenssen, R.[Robert],
Time series cluster kernel for learning similarities between multivariate time series with missing data,
PR(76), No. 1, 2018, pp. 569-581.
Elsevier DOI 1801
Multivariate time series BibRef

Bianchi, F.M.[Filippo Maria], Livi, L.[Lorenzo], Mikalsen, K.Ø.[Karl Øyvind], Kampffmeyer, M.[Michael], Jenssen, R.[Robert],
Learning representations of multivariate time series with missing data,
PR(96), 2019, pp. 106973.
Elsevier DOI 1909
Representation learning, Multivariate time series, Autoencoders, Recurrent neural networks, Kernel methods BibRef

Zhu, Z., Karnik, S., Davenport, M.A., Romberg, J., Wakin, M.B.,
The Eigenvalue Distribution of Discrete Periodic Time-Frequency Limiting Operators,
SPLetters(25), No. 1, January 2018, pp. 95-99.
IEEE DOI 1801
bandlimited signals, discrete Fourier transforms, eigenvalues and eigenfunctions, matrix algebra, time-frequency analysis BibRef

Gong, Z., Li, C., Jiang, F.,
AUV-Aided Joint Localization and Time Synchronization for Underwater Acoustic Sensor Networks,
SPLetters(25), No. 4, April 2018, pp. 477-481.
IEEE DOI 1804
autonomous underwater vehicles, error analysis, mobile communication, nonlinear equations, sensor placement, underwater sensor networks BibRef

de Carvalho Pagliosa, L.[Lucas], Fernandes de Mello, R.[Rodrigo],
Semi-supervised time series classification on positive and unlabeled problems using cross-recurrence quantification analysis,
PR(80), 2018, pp. 53-63.
Elsevier DOI 1805
Time series, Semi-supervised classification, Positive and unlabeled, Self-training, Phase space, Cross-recurrence quantification analysis BibRef

Deng, S.[Song], Yuan, C.A.[Chang-An], Yang, L.C.[Le-Chan], Zhang, L.P.[Li-Ping],
Distributed electricity load forecasting model mining based on hybrid gene expression programming and cloud computing,
PRL(109), 2018, pp. 72-80.
Elsevier DOI 1806
Load forecasting, Artificial fish swarm, Gene expression programming, Cloud computing BibRef

Folgado, D.[Duarte], Barandas, M.[Marília], Matias, R.[Ricardo], Martins, R.[Rodrigo], Carvalho, M.[Miguel], Gamboa, H.[Hugo],
Time Alignment Measurement for Time Series,
PR(81), 2018, pp. 268-279.
Elsevier DOI 1806
Time series, Time warping, Similarity, Distance, Signal alignment BibRef

Li, C.P., Chang, K.J., Chang, H.H., Chen, Y.M.,
Perfect Sequences of Odd Prime Length,
SPLetters(25), No. 7, July 2018, pp. 966-969.
IEEE DOI 1807
correlation methods, cyclic codes, sequential codes, cyclic group, degree-(K + 1) PS, distinct nonzero elements, periodic autocorrelation function (PACF) BibRef

Jing, Y., Liang, J., Zhou, D., So, H.C.,
Spectrally Constrained Unimodular Sequence Design Without Spectral Level Mask,
SPLetters(25), No. 7, July 2018, pp. 1004-1008.
IEEE DOI 1807
optimisation, sequences, spectral analysis, acceleration scheme, algorithm convergence speed, auxiliary variables, spectral level BibRef

Miao, Y., Sun, H., Qi, J.,
Synchro-Compensating Chirplet Transform,
SPLetters(25), No. 9, September 2018, pp. 1413-1417.
IEEE DOI 1809
demodulation, signal processing, time-frequency analysis, transforms, energy compensation, time-frequency transform, time-frequency representation (TFR) BibRef

Gómez-Chova, L.[Luis], Santos-Rodríguez, R.[Raúl], Camps-Valls, G.[Gustau],
Signal-to-noise ratio in reproducing kernel Hilbert spaces,
PRL(112), 2018, pp. 75-82.
Elsevier DOI 1809
Kernel methods, Noise model, Signal-to-noise ratio, SNR, Heteroscedastic, Feature extraction, Signal classification, Causal inference BibRef

Xie, C.[Christopher], Bijral, A.[Avleen], Ferres, J.L.[Juan Lavista],
NonSTOP: A NonSTationary Online Prediction Method for Time Series,
SPLetters(25), No. 10, October 2018, pp. 1545-1549.
IEEE DOI 1810
learning (artificial intelligence), time series, NonSTationary online prediction method, prediction methods, online learning BibRef

Bokde, N.[Neeraj], Beck, M.W.[Marcus W.], Álvarez, F.M.[Francisco Martínez], Kulat, K.[Kishore],
A novel imputation methodology for time series based on pattern sequence forecasting,
PRL(116), 2018, pp. 88-96.
Elsevier DOI 1812
Time series, Imputation, Forecasting, Data mining BibRef

de Souza, D.B.[D. Baptista], Kuhn, E.V., Seara, R.,
A Time-Varying Autoregressive Model for Characterizing Nonstationary Processes,
SPLetters(26), No. 1, January 2019, pp. 134-138.
IEEE DOI 1901
autoregressive processes, covariance matrices, random processes, signal processing, vectors, time-varying autoregressive model, time-varying autoregressive (TVAR) model BibRef

Oregi, I.[Izaskun], Pérez, A.[Aritz], Del Ser, J.[Javier], Lozano, J.A.[Jose A.],
On-line Elastic Similarity Measures for time series,
PR(88), 2019, pp. 506-517.
Elsevier DOI 1901
Time series, Streaming data, Dynamic time warping, Elastic similarity measures BibRef

Jiang, G.X.[Gao-Xia], Wang, W.J.[Wen-Jian], Zhang, W.K.[Wen-Kai],
A novel distance measure for time series: Maximum shifting correlation distance,
PRL(117), 2019, pp. 58-65.
Elsevier DOI 1901
Time series, Distance measure, Second distance, Clustering, Classification BibRef

Gong, Z., Chen, H., Yuan, B., Yao, X.,
Multiobjective Learning in the Model Space for Time Series Classification,
Cyber(49), No. 3, March 2019, pp. 918-932.
IEEE DOI 1902
Time series analysis, Hidden Markov models, Kernel, Data models, Time measurement, Heuristic algorithms, Echo state network (ESN), time series classification BibRef

Marandi, M.K.[Mostafa Khalili], Rave, W.[Wolfgang], Fettweis, G.[Gerhard],
Beam Selection Based on Sequential Competition,
SPLetters(26), No. 3, March 2019, pp. 455-459.
IEEE DOI 1903
error statistics, statistical testing, genie knowledge, beam selection, SNR operating point, sequential test adaptively, generalized likelihood ratio test BibRef

Cheng, Y.[Yao], Haardt, M.[Martin],
Enhanced Direct Fitting Algorithms for PARAFAC2 With Algebraic Ingredients,
SPLetters(26), No. 4, April 2019, pp. 533-537.
IEEE DOI 1903
geometry, matrix algebra, matrix decomposition, numerical analysis, search problems, statistical analysis, tensors, direct fitting algorithm BibRef

Shmaliy, Y.S.[Yuriy S.], Zhao, S.[Shunyi], Ahn, C.K.[Choon Ki],
Optimal and Unbiased Filtering With Colored Process Noise Using State Differencing,
SPLetters(26), No. 4, April 2019, pp. 548-551.
IEEE DOI 1903
Mathematical model, Signal processing algorithms, Kalman filters, Noise measurement, Standards, Real-time systems, unbiased FIR filter BibRef

Alrashdi, A.M.[Ayed M.], Ben Atitallah, I.[Ismail], Al-Naffouri, T.Y.[Tareq Y.],
Precise Performance Analysis of the Box-Elastic Net Under Matrix Uncertainties,
SPLetters(26), No. 5, May 2019, pp. 655-659.
IEEE DOI 1905
Gaussian distribution, Gaussian noise, matrix algebra, mean square error methods, signal processing, box-constraint BibRef

Mehrkam, M.[Mehrrad], Tinati, M.A.[Mohammad Ali], Rezaii, T.Y.[Tohid Yousefi],
Reconstruction of low-rank jointly sparse signals from multiple measurement vectors,
SIViP(13), No. 4, June 2019, pp. 683-691.
Springer DOI 1906
BibRef

Zhang, J., Wei, Z., Yan, Z., Zhou, M., Pani, A.,
Online Change-Point Detection in Sparse Time Series With Application to Online Advertising,
SMCS(49), No. 6, June 2019, pp. 1141-1151.
IEEE DOI 1906
Advertising, Predictive models, Time series analysis, Media, Data models, Noise measurement, Online advertising, sparse time series (TS) BibRef

Delgado, R.A.[Ramón A.], Middleton, R.H.[Richard H.],
Sparse Representation Using Stepwise Tikhonov Regularization With Offline Computations,
SPLetters(26), No. 6, June 2019, pp. 873-877.
IEEE DOI 1906
iterative methods, least squares approximations, signal reconstruction, signal representation, matching pursuit algorithms BibRef

Ramírez-Espinosa, P., Morales-Jimenez, D., Cortés, J.A., Paris, J.F., Martos-Naya, E.,
New Approximation to Distribution of Positive RVs Applied to Gaussian Quadratic Forms,
SPLetters(26), No. 6, June 2019, pp. 923-927.
IEEE DOI 1906
Probability density function, Convergence, Random variables, Signal processing, Distribution functions, Closed-form solutions, statistical distributions BibRef

Mozerov, M.G., Yang, F., van de Weijer, J.,
Sparse Data Interpolation Using the Geodesic Distance Affinity Space,
SPLetters(26), No. 6, June 2019, pp. 943-947.
IEEE DOI 1906
Interpolation, Signal processing algorithms, Image edge detection, Optical imaging, Kernel, Pipelines, adaptive filter BibRef

Abavisani, M., Patel, V.M.,
Deep Sparse Representation-Based Classification,
SPLetters(26), No. 6, June 2019, pp. 948-952.
IEEE DOI 1906
Decoding, Training, Feeds, Encoding, Kernel, Optimization, Testing, Deep learning, sparse representation-based classification, deep sparse representation-based classification BibRef

Roa, N.B.[Nathalie Barbosa], Travé-Massuyès, L.[Louise], Grisales-Palacio, V.H.[Victor H.],
DyClee: Dynamic clustering for tracking evolving environments,
PR(94), 2019, pp. 162-186.
Elsevier DOI 1906
Dynamic clustering, Data mining, On-line learning, Time-series, Data streams, Multi-density clustering BibRef

Zhang, Q.[Qin], Wu, J.[Jia], Zhang, P.[Peng], Long, G.D.[Guo-Dong], Zhang, C.Q.[Cheng-Qi],
Salient Subsequence Learning for Time Series Clustering,
PAMI(41), No. 9, Sep. 2019, pp. 2193-2207.
IEEE DOI 1908
Time series analysis, Feature extraction, Spectral analysis, Analytical models, Labeling, Robustness, Data models, Time series, clustering BibRef

Romanov, E., Ordentlich, O.,
Above the Nyquist Rate, Modulo Folding Does Not Hurt,
SPLetters(26), No. 8, August 2019, pp. 1167-1171.
IEEE DOI 1908
bandlimited signals, information theory, signal reconstruction, signal sampling, discrete-time signal, finite energy signals, unlimited sampling BibRef

Zeng, F.X.[Fan-Xin], He, X.P.[Xi-Ping], Xuan, G.X.[Gui-Xin], Zhang, Z.Y.[Zhen-Yu], Peng, Y.N.[Yan-Ni], Yan, L.[Li],
Perfect Gaussian Integer Sequences Embedding Pre-Given Gaussian Integers,
SPLetters(26), No. 8, August 2019, pp. 1122-1126.
IEEE DOI 1908
Gaussian processes, sequences, pre-given Gaussian integer, perfect GI sequences, PGIS design, arbitrary pre-given GI, perfect Gaussian integer sequences BibRef

Zhang, S.M.[Shui-Mei], Zhang, Y.D.[Yimin D.],
Robust Time-Frequency Analysis of Multiple FM Signals With Burst Missing Samples,
SPLetters(26), No. 8, August 2019, pp. 1172-1176.
IEEE DOI 1908
Hankel matrices, signal reconstruction, time-frequency analysis, time-frequency analysis, burst missing samples, nonstationary signal BibRef

Tian, N.[Nili], Wang, X.L.[Xiao-Ling], Ling, B.W.K.[Bingo Wing-Kuen], Sakalli, M.[Mustafa],
Properties of approximated empirical mode decomposition and optimal design of its system kernel matrix for signal decomposition,
SIViP(13), No. 6, September 2019, pp. 1173-1181.
Springer DOI 1908
BibRef

Zhang, Z.H.[Zhi-Hong], Zhang, G.Z.[Gen-Zhou], Zhang, Z.H.[Zhong-Hao], Chen, G.[Guo], Zeng, Y.B.[Yang-Bin], Wang, B.Z.[Bei-Zhan], Hancock, E.R.[Edwin R.],
Structural network inference from time-series data using a generative model and transfer entropy,
PRL(125), 2019, pp. 357-363.
Elsevier DOI 1909
Transfer entropy, Supergraph, Time series, Network inference, Expectation maximization algorithm BibRef

ur Rehman, N.[Naveed], Khan, B.[Bushra], Naveed, K.[Khuram],
Data-Driven Multivariate Signal Denoising Using Mahalanobis Distance,
SPLetters(26), No. 9, September 2019, pp. 1408-1412.
IEEE DOI 1909
Noise reduction, Covariance matrices, Correlation, Signal processing algorithms, Gaussian noise, multivariate empirical mode decomposition BibRef

Mello, C.E.[Carlos E.], Carvalho, A.S.T.[André S.T.], Lyra, A.[Adria], Pedreira, C.E.[Carlos E.],
Time series classification via divergence measures between probability density functions,
PRL(125), 2019, pp. 42-48.
Elsevier DOI 1909
Time series classification, Kernel methods, Time delay embedding, Kernel density estimation BibRef

Jain, B.[Brijnesh],
Revisiting inaccuracies of time series averaging under dynamic time warping,
PRL(125), 2019, pp. 418-424.
Elsevier DOI 1909
Time series, Dynamic time warping, Time series averaging, k-means BibRef

Baptista de Souza, D.[Douglas], Chanussot, J.[Jocelyn], Favre, A.C.[Anne-Catherine], Borgnat, P.[Pierre],
An Improved Stationarity Test Based on Surrogates,
SPLetters(26), No. 10, October 2019, pp. 1431-1435.
IEEE DOI 1909
Time-frequency analysis, Testing, Feature extraction, Reactive power, Probability density function, Guidelines, surrogates BibRef

Iwana, B.K.[Brian Kenji], Uchida, S.[Seiichi],
Time series classification using local distance-based features in multi-modal fusion networks,
PR(97), 2020, pp. 107024.
Elsevier DOI 1910
Convolutional Neural Network, Time series classification, Dynamic time warping, Distance features BibRef

Brugnoli, E., Toscano, E., Vetro, C.,
Iterative Reconstruction of Signals on Graph,
SPLetters(27), 2020, pp. 76-80.
IEEE DOI 2001
Convergence, Eigenvalues and eigenfunctions, Signal processing algorithms, Laplace equations, spectral analysis BibRef

Saatci, E.[Esra], Saatci, E.[Ertugrul],
Period Determination in Cyclo-Stationary Signals by Autocorrelation and Ramanujan Subspaces,
SPLetters(27), 2020, pp. 266-270.
IEEE DOI 2002
Period determination, cyclo-stationary signals, time-varying cyclic autocorrelation, Ramanujan sums BibRef

Deng, H.Q.[Hui-Qi], Chen, W.[Weifu], Shen, Q.[Qi], Ma, A.J.[Andy J.], Yuen, P.C.[Pong C.], Feng, G.[Guocan],
Invariant subspace learning for time series data based on dynamic time warping distance,
PR(102), 2020, pp. 107210.
Elsevier DOI 2003
Invariant subspace learning, Dynamic time warping (DTW), Time series, Dictionary learning BibRef

Siyou Fotso, V.S.[Vanel Steve], Mephu Nguifo, E.[Engelbert], Vaslin, P.[Philippe],
Frobenius correlation based u-shapelets discovery for time series clustering,
PR(103), 2020, pp. 107301.
Elsevier DOI 2005
Clustering, UShapelet, Correlation, Time series BibRef

Mahjoub, C.[Chahira], Bellanger, J.J.[Jean-Jacques], Kachouri, A.[Abdennaceur], Jeannès, R.L.[Régine Le_Bouquin],
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SIViP(14), No. 5, July 2020, pp. 945-953.
Springer DOI 2006
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An Exploratory Method for Smooth/Transient Decomposition,
SPLetters(27), 2020, pp. 890-894.
IEEE DOI 2006
Constrained filtering, gaussian process, signal decomposition, empirical mode decomposition, vital signs monitoring, radar BibRef

Carmona-Poyato, Á.[Ángel], Fernández-García, N.L.[Nicolás Luis], Madrid-Cuevas, F.J.[Francisco José], Durán-Rosal, A.M.[Antonio Manuel],
A new approach for optimal time-series segmentation,
PRL(135), 2020, pp. 153-159.
Elsevier DOI 2006
Data representation, Data compression, Optimal time series segmentation, Time series size reduction BibRef

Shin, D., Cho, H., Yang, I.,
Power-Law Processor Over Segmentation for Variable Length Transients Detection,
SPLetters(27), 2020, pp. 1065-1069.
IEEE DOI 2007
Detectors, Transient analysis, Signal to noise ratio, Frequency-domain analysis, Time-domain analysis, segmentation BibRef

Cosentino, Balestriero, R.[Randall], Baraniuk, R.G.[Richard G.], Aazhang, B.[Behnaam],
Universal Frame Thresholding,
SPLetters(27), 2020, pp. 1115-1119.
IEEE DOI 2007
Upper bound, Scattering, Signal processing algorithms, Computational complexity, Discrete wavelet transforms, Bird song, wavelet BibRef

Clarkson, I.V.L., Sirianunpiboon, S., Howard, S.D.,
Frequency Estimation in Coherent, Periodic Pulse Trains,
SPLetters(27), 2020, pp. 1415-1419.
IEEE DOI 2009
Frequency estimation, Shape, Signal to noise ratio, Coherence, Frequency modulation, Estimation, Transmitters, Doppler radar, radar measurements BibRef

Ye, R.[Rui], Dai, Q.[Qun],
Implementing transfer learning across different datasets for time series forecasting,
PR(109), 2021, pp. 107617.
Elsevier DOI 2009
Time series prediction, Deep learning, Transfer learning, Convolutional neural network (CNN) BibRef

Zhou, Y., Ding, F.,
Modeling Nonlinear Processes Using the Radial Basis Function-Based State-Dependent Autoregressive Models,
SPLetters(27), 2020, pp. 1600-1604.
IEEE DOI 2009
Signal processing algorithms, Parameter estimation, Technological innovation, Mathematical model, data saturation BibRef

Türkmen, A.C.[Ali Caner], Çapan, G.[Gökhan], Cemgil, A.T.[Ali Taylan],
Clustering Event Streams With Low Rank Hawkes Processes,
SPLetters(27), 2020, pp. 1575-1579.
IEEE DOI 2009
e.g. neural spikes. Clustering algorithms, Numerical stability, Machine learning algorithms, Signal processing algorithms, clustering BibRef

Tanisaro, P., Heidemann, G.,
A very concise feature representation for time series classification understanding,
MVA19(1-6)
DOI Link 1911
data analysis, feature extraction, pattern classification, random forests, recurrent neural nets, time series, Feature extraction BibRef

Boubrahimi, S.F., Ma, R., Aydin, B., Hamdi, S.M., Angryk, R.,
Scalable kNN Search Approximation for Time Series Data,
ICPR18(970-975)
IEEE DOI 1812
Univariate Time Series classification, Scalable Nearest Neighbor Search, Density-Based Clustering BibRef

Han, Y., Zhang, S., Geng, Z.,
Multi-Frequency Decomposition with Fully Convolutional Neural Network for Time Series Classification,
ICPR18(284-289)
IEEE DOI 1812
Feature extraction, Time series analysis, Convolution, Neural networks, Discrete Fourier transforms, Kernel, Multi-Frequency decomposition BibRef

Zhao, R., Schalk, G., Ji, Q.,
Temporal Pattern Localization using Mixed Integer Linear Programming,
ICPR18(1361-1365)
IEEE DOI 1812
Time series analysis, Hidden Markov models, Data collection, Optimization, Probabilistic logic, Pattern recognition BibRef

Caglar, I.[Ibrahim], Hancock, E.R.[Edwin R.],
Graph Time Series Analysis Using Transfer Entropy,
SSSPR18(217-226).
Springer DOI 1810
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González-Vanegas, W.[Wilson], Alvarez-Meza, A.[Andrés], Orozco-Gutierrez, Á.[Álvaro],
Sparse Hilbert Embedding-Based Statistical Inference of Stochastic Ecological Systems,
CIARP17(255-262).
Springer DOI 1802
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Singh, P.[Pritpal], Dhiman, G.[Gaurav],
A Fuzzy-LP Approach in Time Series Forecasting,
PReMI17(243-253).
Springer DOI 1711
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Chirikjian, G.S.[Gregory S.],
Signal Classification in Quotient Spaces via Globally Optimal Variational Calculus,
Diff-CVML17(735-743)
IEEE DOI 1709
Computer vision, Pattern recognition, Zinc BibRef

Ayech, M.W.[Mohamed Walid], Ziou, D.[Djemel],
K-Autoregressive Clustering: Application on Terahertz Image Analysis,
ICIAR17(145-152).
Springer DOI 1706
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Zhao, R.[Rui], Schalk, G.[Gerwin], Ji, Q.[Qiang],
Robust signal identification for dynamic pattern classification,
ICPR16(3910-3915)
IEEE DOI 1705
Computational modeling, Data models, Hidden Markov models, Motion segmentation, Robustness, Testing, Time, series, analysis BibRef

Dupont, M., Marteau, P.F., Ghouaiel, N.,
Detecting low-quality reference time series in stream recognition,
ICPR16(2556-2561)
IEEE DOI 1705
Big Data, Measurement, Pattern recognition, Sensors, Testing, Time series analysis, Training BibRef

Zhao, R.[Rui], Iqbal, M.R.A., Bennett, K.P., Ji, Q.A.[Qi-Ang],
Wind turbine fault prediction using soft label SVM,
ICPR16(3192-3197)
IEEE DOI 1705
Economic indicators, Sensors, Support vector machines, Time series analysis, Training, Wind energy, Wind, turbines BibRef

Ye, C., Wilson, R.C., Hancock, E.R.[Edwin R.],
Analyzing graph time series using a generative model,
ICPR16(3338-3343)
IEEE DOI 1705
Analytical models, Complexity theory, Computational modeling, Data models, Entropy, Probabilistic logic, Probability, distribution BibRef

Ridi, A., Gisler, C., Hennebert, J.,
Aggregation procedure of Gaussian Mixture Models for additive features,
ICPR16(2544-2549)
IEEE DOI 1705
Additives, Biological system modeling, Computational modeling, Covariance matrices, Hidden Markov models, Time series analysis, Training BibRef

Marcacini, R.M., Carnevali, J.C., Domingos, J.,
On combining Websensors and DTW distance for kNN Time Series Forecasting,
ICPR16(2521-2525)
IEEE DOI 1705
Biological system modeling, Forecasting, Knowledge engineering, Mathematical model, Predictive models, Time measurement, Time, series, analysis BibRef

Kulczycki, P.[Piotr], Charytanowicz, M.[Malgorzata], Kowalski, P.A.[Piotr A.], Lukasik, S.[Szymon],
Atypical (Rare) Elements Detection: A Conditional Nonparametric Approach,
CompIMAGE16(56-64).
Springer DOI 1704
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Saidane, Y.[Yosra], Ben Jebara, S.[Sofia],
EMG signal analysis for comprehension of genders differences behavior during pre-motor activity,
ISIVC16(325-330)
IEEE DOI 1704
Correlation BibRef

Lo Presti, L.[Liliana], La Cascia, M.[Marco],
A Novel Time Series Kernel for Sequences Generated by LTI Systems,
ACCV16(III: 433-451).
Springer DOI 1704
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Guo, L., Liew, A.W.C.,
Online-Offline Extreme Learning Machine with Concept Drift Tracking for Time Series Data,
DICTA16(1-6)
IEEE DOI 1701
Data models BibRef

Seversky, L.M.[Lee M.], Davis, S.[Shelby], Berger, M.[Matthew],
On Time-Series Topological Data Analysis: New Data and Opportunities,
DIFF-CV16(1014-1022)
IEEE DOI 1612
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Bascol, K.[Kevin], Emonet, R.[Rémi], Fromont, E.[Elisa], Odobez, J.M.[Jean-Marc],
Unsupervised Interpretable Pattern Discovery in Time Series Using Autoencoders,
SSSPR16(427-438).
Springer DOI 1611
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Vuksanovic, B., Pota, H.,
Resonant modes analysis in power systems algorithms and Matlab GUI,
ICIVC16(129-134)
IEEE DOI 1610
graphical user interfaces BibRef

Molaei, S.M., Keyvanpour, M.R.,
An analytical review for event prediction system on time series,
IPRIA15(1-6)
IEEE DOI 1603
data mining BibRef

Ogryczak, W.[Wlodzimierz], Hurkala, J.[Jaroslaw],
Determining OWA Operator Weights by Maximum Deviation Minimization,
PReMI15(335-344).
Springer DOI 1511
OWA: ordered weighted averaging BibRef

Turner, L.D.[Liam D.], Allen, S.M.[Stuart M.], Whitaker, R.M.[Roger M.],
Push or Delay? Decomposing Smartphone Notification Response Behaviour,
HBUI15(69-83).
Springer DOI 1511
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Acuña, D.[Diego], Allende-Cid, H.[Héctor], Allende, H.[Héctor],
The Effect of Innovation Assumptions on Asymmetric GARCH Models for Volatility Forecasting,
CIARP15(527-534).
Springer DOI 1511
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Abughali, I.K.A.[Ibrahim K. A.], Minz, S.[Sonajharia],
Binarizing Change for Fast Trend Similarity Based Clustering of Time Series Data,
PReMI15(257-267).
Springer DOI 1511
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Rodriguez, F.[Fernanda], Di Martino, M.[Matías], Kosut, J.P.[Juan Pablo], Santomauro, F.[Fernando], Lecumberry, F.[Federico], Fernández, A.[Alicia],
Optimal and Linear F-Measure Classifiers Applied to Non-technical Losses Detection,
CIARP15(83-91).
Springer DOI 1511
power supply companies. BibRef

Ho, C.C.[Chiung Ching], Ting, C.Y.[Choo-Yee],
Time Series Analysis and Forecasting of Dengue Using Open Data,
IVIC15(51-63).
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Single-Channel Separation Between Stationary and Non-stationary Signals Using Relevant Information,
IbPRIA15(452-459).
Springer DOI 1506
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Atto, A.M.[Abdourrahmane M.], Fillatre, L.[Lionel], Antonini, M.[Marc], Nikiforov, I.[Igor],
Simulation of image time series from dynamical fractional brownian fields,
ICIP14(6086-6090)
IEEE DOI 1502
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Cabrai, R.[Ricardo], Costeira, J.P.[Joao P.], Bernardino, A.[Alexandre], de la Torre, F.[Fernando],
Optimal no-intersection multi-label binary localization for time series using totally unimodular linear programming,
ICIP14(3127-3130)
IEEE DOI 1502
Computer vision BibRef

Damoulas, T.[Theodoros], He, J.[Jin], Bernstein, R.[Rich], Gomes, C.P.[Carla P.], Arora, A.[Anish],
String Kernels for Complex Time-Series: Counting Targets from Sensed Movement,
ICPR14(4429-4434)
IEEE DOI 1412
Approximation methods BibRef

Bargi, A.[Ava], Xu, R.Y.D.[Richard Yi Da], Piccardi, M.[Massimo],
An Infinite Adaptive Online Learning Model for Segmentation and Classification of Streaming Data,
ICPR14(3440-3445)
IEEE DOI 1412
Accuracy BibRef

Martinez-Vargas, J.D., Castro-Hoyos, C., Alvarez-Meza, A.M., Acosta-Medina, C.D., Castellanos-Domínguez, C.G.[Cesar German],
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ICPR14(3469-3474)
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Evaluation of Time Series Distance Functions in the Task of Detecting Remote Phenology Patterns,
ICPR14(3126-3131)
IEEE DOI 1412
Accuracy BibRef

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Time Series Transductive Classification on Imbalanced Data Sets: An Experimental Study,
ICPR14(3780-3785)
IEEE DOI 1412
Algorithm design and analysis BibRef

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Extracting Texture Features for Time Series Classification,
ICPR14(1425-1430)
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Bad Data Analysis with Sparse Sensors for Leak Localisation in Water Distribution Networks,
ICPR14(3642-3647)
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Statistical Anomaly Detection in Mean and Variation of Energy Consumption,
ICPR14(3570-3575)
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Buildings BibRef

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A Two-Stage Regression Using Bioimpedance and Temperature for Hydration Assessment During Sports,
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Time-Frequency Analysis for Nonlinear Lamb Wave Signal,
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IEEE DOI 0910
BibRef

Hautamaki, V.[Ville], Nykanen, P.[Pekka], Franti, P.[Pasi],
Time-series clustering by approximate prototypes,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Venkataramana, K.B.[Kini B.], Sekhar, C.C.[C. Chandra],
Large margin AR model for time series classification,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Batyrshin, I.[Ildar], Solovyev, V.[Valery],
Positive and Negative Local Trend Association Patterns in Analysis of Associations between Time Series,
MCPR14(92-101).
Springer DOI 1407
BibRef
Earlier: A1, Only:
Up and Down Trend Associations in Analysis of Time Series Shape Association Patterns,
MCPR12(246-254).
Springer DOI 1208
BibRef

Batyrshin, I.[Ildar], Sheremetov, L.[Leonid],
Time Series Pattern Recognition Based on MAP Transform and Local Trend Associations,
CIARP06(910-919).
Springer DOI 0611
MAP: Moving Approximation Transform. BibRef

Gerek, Ö.N.[Ömer N.], Ece, D.G.,
A 2D representation for analysis and coding of power quality events,
ICIP03(III: 561-564).
IEEE DOI 0312
BibRef

Novak, D., Lhotska, L., Al-Ani, T., Hamam, Y., Cuesta-Frau, D., Mico, P., Aboy, M.,
Morphology analysis of physiological signals using hidden Markov models,
ICPR04(III: 754-757).
IEEE DOI 0409
BibRef

Sankur, B., Kahya, Y.P., Guler, E.C., Engin, T.,
Feature extraction and classification of nonstationary signals based on the multiresolution signal decomposition,
ICPR94(B:592-595).
IEEE DOI 9410
BibRef

Chapter on New Unsorted Entries, and Other Miscellaneous Papers continues in
Time Series Analysis, Recovery, Restoration, Estimation .