26.1.5.2 Time Series Analysis, Recovery, Restoration, Prediction, Reconstruction, Forecast, Estimation

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

Göken, Ç., Gezici, S.,
Optimal Parameter Encoding Based on Worst Case Fisher Information Under a Secrecy Constraint,
SPLetters(24), No. 11, November 2017, pp. 1611-1615.
IEEE DOI 1710
encoding, mean square error methods, linear minimum MSE estimator, low-complexity algorithm, BibRef

Wang, P., Orlik, P.V., Sadamoto, K., Tsujita, W., Gini, F.,
Parameter Estimation of Hybrid Sinusoidal FM-Polynomial Phase Signal,
SPLetters(24), No. 1, January 2017, pp. 66-70.
IEEE DOI 1702
frequency modulation BibRef

Arriaga-Trejo, I.A., Orozco-Lugo, A.G., Villanueva-Maldonado, J., Flores-Troncoso, J.,
Joint I/Q imbalances estimation using data-dependent superimposed training,
SIViP(11), No. 4, May 2017, pp. 729-736.
Springer DOI 1704
Joint estimation of the channel impulse response and frequency-dependent in-phase and quadrature-phase (I/Q) imbalances. 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

Baltazar-Raigosa, A.[Antonio],
Identification of Widely Linear Systems Using Data-Dependent Superimposed Training,
SPLetters(29), 2022, pp. 2133-2137.
IEEE DOI 2211
Training, Estimation, Mathematical models, Nonlinear distortion, Finite impulse response filters, Channel estimation, complementary autocorrelation 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

Wang, P., Orlik, P.V., Sadamoto, K., Tsujita, W., Sawa, Y.,
Cramer-Rao Bounds for a Coupled Mixture of Polynomial Phase and Sinusoidal FM Signals,
SPLetters(24), No. 6, June 2017, pp. 746-750.
IEEE DOI 1609
polynomials, signal processing, CRB, Crame´r-Rao bounds, polynomial phase signal, pure PPS case, sinusoidal FM signals, Doppler radar, Frequency modulation, Indexes, Mixture models, Parameter estimation. BibRef

Tobar, F., Rios, G., Valdivia, T., Guerrero, P.,
Recovering Latent Signals From a Mixture of Measurements Using a Gaussian Process Prior,
SPLetters(24), No. 2, February 2017, pp. 231-235.
IEEE DOI 1702
Bayes methods BibRef

Wang, W.D.[Wen-Dong], Wang, J.J.[Jian-Jun], Zhang, Z.L.[Zi-Li],
Robust Signal Recovery With Highly Coherent Measurement Matrices,
SPLetters(24), No. 3, March 2017, pp. 304-308.
IEEE DOI 1702
Approximation algorithms BibRef

Chen, S.J.[Shao-Jie], Liu, K.[Kai], Yang, Y.G.[Yu-Guang], Xu, Y.T.[Yu-Ting], Lee, S.[Seonjoo], Lindquist, M.[Martin], Caffo, B.S.[Brian S.], Vogelstein, J.T.[Joshua T.],
An M-estimator for reduced-rank system identification,
PRL(86), No. 1, 2017, pp. 76-81.
Elsevier DOI 1702
High dimensional time-series data. BibRef

Bendory, T., Eldar, Y.C.,
Recovery of Sparse Positive Signals on the Sphere from Low Resolution Measurements,
SPLetters(22), No. 12, December 2015, pp. 2383-2386.
IEEE DOI 1512
convex programming BibRef

Wan, Z.[Zhong], Guo, J.[Jie], Liu, J.J.[Jing-Jing], Liu, W.Y.[Wei-Yi],
A modified spectral conjugate gradient projection method for signal recovery,
SIViP(12), No. 8, November 2018, pp. 1455-1462.
Springer DOI 1809
Signal recovery. BibRef

Perotti, L.C., Vrinceanu, D., Bessis, D.,
Recovery of the Starting Times of Delayed Signals,
SPLetters(25), No. 10, October 2018, pp. 1455-1459.
IEEE DOI 1810
iterative methods, signal processing, smoothing methods, starting times, delayed signals, starting point, arbitrary number, Padé approximant BibRef

Bey, N.Y.[Nourédine Yahya],
Highly accurate frequency estimation of brief duration signals in noise,
SIViP(12), No. 7, October 2018, pp. 1279-1283.
WWW Link. 1809
BibRef

Horstmann, S., Ramírez, D., Schreier, P.J.,
Joint Detection of Almost-Cyclostationary Signals and Estimation of Their Cycle Period,
SPLetters(25), No. 11, November 2018, pp. 1695-1699.
IEEE DOI 1811
channel bank filters, interpolation, signal detection, signal sampling, cycle period, wide-sense stationary noise, sample rate conversion 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

Wang, X., Li, G., Varshney, P.K.,
Distributed Detection of Weak Signals From One-Bit Measurements Under Observation Model Uncertainties,
SPLetters(26), No. 3, March 2019, pp. 415-419.
IEEE DOI 1903
maximum likelihood estimation, quantisation (signal), signal detection, wireless sensor networks, one-bit data, locally most powerful test BibRef

Khan, N.A.[Nabeel Ali], Mohammadi, M.[Mokhtar], Ali, S.[Sadiq],
Instantaneous frequency estimation of intersecting and close multi-component signals with varying amplitudes,
SIViP(13), No. 3, April 2019, pp. 517-524.
Springer DOI 1904
BibRef

Khan, S.A., Saleem, S., Hassan, S.A., Ilyas, M.U.,
An Improved Data-Aided Linear Estimator of Modulation Index for Binary CPM Signals,
SPLetters(26), No. 5, May 2019, pp. 780-784.
IEEE DOI 1905
continuous phase modulation, error statistics, estimation algorithm, partial response BibRef

Nichols, J.M., Hutchinson, M.N., Menkart, N., Cranch, G.A., Rohde, G.K.,
Time Delay Estimation Via Wasserstein Distance Minimization,
SPLetters(26), No. 6, June 2019, pp. 908-912.
IEEE DOI 1906
computational complexity, delay estimation, minimisation, signal processing, linear time, time delay estimation, Wasserstein distance BibRef

Gan, M., Chen, X., Ding, F., Chen, G., Chen, C.L.P.,
Adaptive RBF-AR Models Based on Multi-Innovation Least Squares Method,
SPLetters(26), No. 8, August 2019, pp. 1182-1186.
IEEE DOI 1908
autoregressive processes, learning (artificial intelligence), least mean squares methods, parameter estimation, time series prediction BibRef

Deprem, Z.[Zeynel], Çetin, A.E.[A. Enis], Arikan, O.[Orhan],
AM/FM signal estimation with micro-segmentation and polynomial fit,
SIViP(8), No. 3, March 2014, pp. 399-413.
Springer DOI 1403
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

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

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

Abavisani, M., Patel, V.M.,
Deep Multimodal Sparse Representation-Based Classification,
ICIP20(773-777)
IEEE DOI 2011
Training, Sparse matrices, Decoding, Encoding, Data models, Testing, Image reconstruction, Sparse representation, deep multimodal sparse representation classification 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

Zhang, S.M.[Shui-Mei], Zhang, Y.M.D.[Yi-Min 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

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

Muniraju, G., Tepedelenlioglu, C., Spanias, A.,
Consensus Based Distributed Spectral Radius Estimation,
SPLetters(27), 2020, pp. 1045-1049.
IEEE DOI 2007
Convergence, Eigenvalues and eigenfunctions, Packet loss, Estimation, Distributed algorithms, Signal processing algorithms, spectral radius BibRef

Mohammadi, E., Gohari, A., Marvasti, F.,
A Square Root Sampling Law for Signal Recovery,
SPLetters(26), No. 4, April 2019, pp. 562-566.
IEEE DOI 1903
Noise measurement, Distortion, Distortion measurement, Stochastic processes, Atmospheric measurements, square root law BibRef

Dlask, M.[Martin], Kukal, J.[Jaromir],
Hurst exponent estimation from short time series,
SIViP(13), No. 2, March 2019, pp. 263-269.
Springer DOI 1904
Time series. BibRef

Fosson, S.M., Abuabiah, M.,
Recovery of Binary Sparse Signals From Compressed Linear Measurements via Polynomial Optimization,
SPLetters(26), No. 7, July 2019, pp. 1070-1074.
IEEE DOI 1906
Optimization, Noise measurement, Sparse matrices, Compressed sensing, Image coding, Sensors, Programming, sparse signal recovery BibRef

Guo, J., Chen, H., Chen, S.,
Improved Kernel Recursive Least Squares Algorithm Based Online Prediction for Nonstationary Time Series,
SPLetters(27), 2020, pp. 1365-1369.
IEEE DOI 2008
Signal processing algorithms, Kernel, Prediction algorithms, Heuristic algorithms, Time series analysis, Dictionaries, quantized kernel recursive least squares BibRef

Nie, D., Xie, K., Zhou, F., Qiao, G.,
A Correlation Detection Method of Low SNR Based on Multi-Channelization,
SPLetters(27), 2020, pp. 1375-1379.
IEEE DOI 2008
Correlation, Signal to noise ratio, Signal processing algorithms, Signal detection, Detection algorithms, Analytical models, time-varying signal BibRef

Garg, K., Baranwal, M.,
CAPPA: Continuous-Time Accelerated Proximal Point Algorithm for Sparse Recovery,
SPLetters(27), 2020, pp. 1760-1764.
IEEE DOI 2010
Convergence, Heuristic algorithms, Signal processing algorithms, Machine learning algorithms, Convex functions, Acceleration, signal reconstruction BibRef

Ilic, I.[Igor], Görgülü, B.[Berk], Cevik, M.[Mucahit], Baydogan, M.G.[Mustafa Gökçe],
Explainable boosted linear regression for time series forecasting,
PR(120), 2021, pp. 108144.
Elsevier DOI 2109
Time series regression, Probabilistic forecasting, Decision trees, Linear regression, ARIMA BibRef

Bandara, K.[Kasun], Hewamalage, H.[Hansika], Liu, Y.H.[Yuan-Hao], Kang, Y.F.[Yan-Fei], Bergmeir, C.[Christoph],
Improving the accuracy of global forecasting models using time series data augmentation,
PR(120), 2021, pp. 108148.
Elsevier DOI 2109
Time series forecasting, Global forecasting models, Data augmentation, Transfer learning, RNN BibRef

Hewamalage, H.[Hansika], Bergmeir, C.[Christoph], Bandara, K.[Kasun],
Global models for time series forecasting: A Simulation study,
PR(124), 2022, pp. 108441.
Elsevier DOI 2203
Time series forecasting, Global forecasting models, Time series simulation, Data generating processes BibRef

Harel, N.[Nadav], Routtenberg, T.[Tirza],
Bayesian Post-Model-Selection Estimation,
SPLetters(28), 2021, pp. 175-179.
IEEE DOI 2102
Estimation, Bayes methods, Probability density function, Parameter estimation, Computational modeling, sparse recovery BibRef

Berman, I.E.[Itai E.], Routtenberg, T.[Tirza],
Partially Linear Bayesian Estimation Using Mixed-Resolution Data,
SPLetters(28), 2021, pp. 2202-2206.
IEEE DOI 2112
Q measurement, Estimation, Quantization (signal), Closed-form solutions, Channel estimation, mixed-resolution data BibRef

Cheng, Y.Y.[Yi-Yao], Liu, L.[Lei], Ping, L.[Li],
An Integral-Based Approach to Orthogonal AMP,
SPLetters(28), 2021, pp. 194-198.
IEEE DOI 2102
Monte Carlo methods, Training, Signal processing algorithms, Binary phase shift keying, Software algorithms, Sensors, state evolution BibRef

Rupniewski, M.W.,
Reconstruction of Periodic Signals From Asynchronous Trains of Samples,
SPLetters(28), 2021, pp. 289-293.
IEEE DOI 2102
Random variables, Signal reconstruction, Signal processing algorithms, Sensors, Probabilistic logic, Shape, signal sampling BibRef

Øyvind Mikalsen, K.[Karl], Soguero-Ruiz, C.[Cristina], Maria Bianchi, F.[Filippo], Revhaug, A.[Arthur], Jenssen, R.[Robert],
Time series cluster kernels to exploit informative missingness and incomplete label information,
PR(115), 2021, pp. 107896.
Elsevier DOI 2104
Multivariate time series, Kernel methods, Missing data, Informative missingness, Semi-supervised learning BibRef

Xie, C.L.[Chun-Lei], Sun, Y.[Yu], Ming, Y.[Yang],
Constructions of Optimal Binary Z-Complementary Sequence Sets With Large Zero Correlation Zone,
SPLetters(28), 2021, pp. 1694-1698.
IEEE DOI 2109
Boolean functions, Zero correlation zone, Correlation, Sun, Companies, Systematics, Research and development, zero correlation zone BibRef

Lai, H.D.[Hua-Dong], Xu, W.C.[Wei-Chao], Dai, J.S.[Ji-Sheng], Zhou, Y.Z.[Yan-Zhou],
Threshold Setting of Generalized Likelihood Ratio Test for Impropriety of Complex Signals,
SPLetters(28), 2021, pp. 1699-1703.
IEEE DOI 2109
Covariance matrices, Transforms, Simulation, Signal processing algorithms, Random variables, impropriety test BibRef

Haley, C.,
Missing-Data Nonparametric Coherency Estimation,
SPLetters(28), 2021, pp. 1704-1708.
IEEE DOI 2109
Coherence, Time series analysis, Frequency estimation, Smoothing methods, Recruitment, Oscillators, Indexes, Coherence, power spectrum 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

Song, X.X.[Xiao-Xiang], Guo, Y.[Yan], Li, N.[Ning], Zhang, L.X.[Li-Xiong],
Integrated Online Prediction Model for IoT Data,
SPLetters(28), 2021, pp. 2043-2047.
IEEE DOI 2111
Predictive models, Data models, Time series analysis, Linear programming, Training, Time-domain analysis, time series BibRef

Cao, Z., Dai, J., Xu, W., Chang, C.,
Fast Variational Bayesian Inference for Temporally Correlated Sparse Signal Recovery,
SPLetters(28), 2021, pp. 214-218.
IEEE DOI 2102
Bayes methods, Correlation, Signal processing algorithms, Sparse matrices, Biomedical measurement, Time measurement, variational Bayesian inference (VBI) BibRef

He, Z.[Zihao], He, H.Y.[Hong-Yu], Liu, X.L.[Xiao-Li], Wen, J.M.[Jin-Ming],
An Improved Sufficient Condition for Sparse Signal Recovery With Minimization of L1-L2,
SPLetters(29), 2022, pp. 907-911.
IEEE DOI 2205
Coherence, Upper bound, Minimization, Matching pursuit algorithms, Information science, Noise measurement, Wireless communication, L_1-L_2-minimization BibRef

Liu, X.Q.[Xiao-Qian], Chi, E.C.[Eric C.],
Revisiting convexity-preserving signal recovery with the linearly involved GMC penalty,
PRL(156), 2022, pp. 60-66.
Elsevier DOI 2205
Convexity-preserving nonconvex strategy, Generalized minimax concave penalty, Saddle-point problem BibRef

Riba, J.[Jaume], Vilà, M.[Marc],
On Infinite Past Predictability of Cyclostationary Signals,
SPLetters(29), 2022, pp. 647-651.
IEEE DOI 2203
Signal processing, Eigenvalues and eigenfunctions, Coherence, Autocorrelation, Wiener filters, Matrix decomposition, Modulation, spectral coherence BibRef

Ramakrishna, A.[Anil], Gupta, R.[Rahul], Narayanan, S.[Shrikanth],
Joint Multi-Dimensional Model for Global and Time-Series Annotations,
AffCom(13), No. 1, January 2022, pp. 473-484.
IEEE DOI 2203
Annotations, Task analysis, Predictive models, Time series analysis, Mathematical model, Computational modeling, factor analysis BibRef

Liu, Y.F.[Yu-Feng], Zhu, Z.B.[Zhi-Bin], Zhang, B.X.[Ben-Xin],
Improved iteratively reweighted least squares algorithms for sparse recovery problem,
IET-IPR(16), No. 5, 2022, pp. 1324-1340.
DOI Link 2203
BibRef

Pang, Y.[Yue], Zhou, X.D.[Xiang-Dong], Zhang, J.Q.[Jun-Qi], Sun, Q.[Quan], Zheng, J.B.[Jian-Bin],
Hierarchical electricity time series prediction with cluster analysis and sparse penalty,
PR(126), 2022, pp. 108555.
Elsevier DOI 2204
Hierarchical time series forecasting, Data mining, Machine learning BibRef

Chen, X.Y.[Xin-Yu], Sun, L.J.[Li-Jun],
Bayesian Temporal Factorization for Multidimensional Time Series Prediction,
PAMI(44), No. 9, September 2022, pp. 4659-4673.
IEEE DOI 2208
Time series analysis, Data models, Bayes methods, Spatiotemporal phenomena, Tensors, Reactive power, Markov chain Monte Carlo (MCMC) BibRef

Liu, Y.[Yang], Wang, Z.[Zheng], Yu, X.Y.[Xin-Yang], Chen, X.[Xin], Sun, M.[Meijun],
Memory-based Transformer with shorter window and longer horizon for multivariate time series forecasting,
PRL(160), 2022, pp. 26-33.
Elsevier DOI 2208
Multivariate time series, Long-term forecasting, Transformer, Deep learning BibRef

Koç, E.[Emirhan], Koç, A.[Aykut],
Fractional Fourier Transform in Time Series Prediction,
SPLetters(29), 2022, pp. 2542-2546.
IEEE DOI 2301
Time series analysis, Feature extraction, Decoding, Fourier transforms, Training, Wavelet transforms, decoder BibRef

Huang, J.W.[Jian-Wen], Zhang, F.[Feng], Jia, J.P.[Jin-Ping],
A New Sufficient Condition for Non-Convex Sparse Recovery via Weighted L_r-L_1 Minimization,
SPLetters(29), 2022, pp. 1555-1558.
IEEE DOI 2208
Minimization, Sparse matrices, Pollution measurement, Compressed sensing, Indexes, Technological innovation, nonconvex sparse recovery BibRef

Semenoglou, A.A.[Artemios-Anargyros], Spiliotis, E.[Evangelos], Assimakopoulos, V.[Vassilios],
Data augmentation for univariate time series forecasting with neural networks,
PR(134), 2023, pp. 109132.
Elsevier DOI 2212
Time series, Forecasting, Data augmentation, Neural networks, M4 competition BibRef

Le Guen, V.[Vincent], Thome, N.[Nicolas],
Deep Time Series Forecasting With Shape and Temporal Criteria,
PAMI(45), No. 1, January 2023, pp. 342-355.
IEEE DOI 2212
Forecasting, Shape, Probabilistic logic, Time series analysis, Predictive models, Training, Kernel, Time series forecasting, determinantal point processes BibRef

Moreno-Pino, F.[Fernando], Olmos, P.M.[Pablo M.], Artés-Rodríguez, A.[Antonio],
Deep autoregressive models with spectral attention,
PR(133), 2023, pp. 109014.
Elsevier DOI 2210
Attention models, Deep learning, Filtering, Global-local contexts, Signal processing, Time series forecasting BibRef

Money, R.[Rohan], Krishnan, J.[Joshin], Beferull-Lozano, B.[Baltasar], Isufi, E.[Elvin],
Online Edge Flow Imputation on Networks,
SPLetters(30), 2023, pp. 115-119.
IEEE DOI 2303
Signal processing algorithms, Laplace equations, Optimization, Time series analysis, Signal reconstruction, Reactive power, simplicial complex BibRef

Xie, L.J.[Lie-Jun],
Improved RIC Bounds in Terms of delta_2s for Hard Thresholding-Based Algorithms,
SPLetters(30), 2023, pp. 21-25.
IEEE DOI 2302
Signal processing algorithms, Sensors, Indexes, Partitioning algorithms, Linear systems, Iterative algorithms, sparse recovery algorithm BibRef

Liu, N.[Naihao], Wang, J.Y.[Jing-Yu], Yang, Y.[Yang], Li, Z.[Zhen], Gao, J.H.[Jing-Huai],
WVDNet: Time-Frequency Analysis via Semi-Supervised Learning,
SPLetters(30), 2023, pp. 55-59.
IEEE DOI 2302
Training, Data models, Predictive models, Kernel, Training data, Time-frequency analysis, Task analysis, Deep learning, wigner-ville distribution BibRef

Li, Z.L.[Zhuo Lin], Zhang, G.W.[Gao Wei], Yu, J.[Jie], Xu, L.Y.[Ling Yu],
Dynamic graph structure learning for multivariate time series forecasting,
PR(138), 2023, pp. 109423.
Elsevier DOI 2303
Multivariate time series forecasting, Dynamic spatio-temporal dependencies, Graph neural networks, Graph structure learning BibRef

Dhake, H.[Harshal], Kashyap, Y.[Yashwant], Kosmopoulos, P.[Panagiotis],
Algorithms for Hyperparameter Tuning of LSTMs for Time Series Forecasting,
RS(15), No. 8, 2023, pp. 2076.
DOI Link 2305
BibRef

Zhan, F.[Fei], Zhou, X.F.[Xiao-Feng], Li, S.[Shuai], Jia, D.[Dongni], Song, H.[Hong],
Learning Latent ODEs With Graph RNN for Multi-Channel Time Series Forecasting,
SPLetters(30), 2023, pp. 1432-1436.
IEEE DOI 2310
BibRef

Almeida, A.[Ana], Brás, S.[Susana], Sargento, S.[Susana], Pinto, F.C.[Filipe Cabral],
Time Series Imputation in Faulty Systems,
IbPRIA23(28-39).
Springer DOI 2307
BibRef

Pang, J.[Jie], Gao, B.[Bo],
Application of a Randomized Algorithm for Extracting a Shallow Low-Rank Structure in Low-Frequency Reverberation,
RS(15), No. 14, 2023, pp. 3648.
DOI Link 2307
BibRef

Huo, L.[Limei], Chen, W.[Wengu], Ge, H.M.[Huan-Min], Ng, M.K.[Michael K.],
L_1 beta L_q Minimization for Signal and Image Recovery,
SIIMS(16), No. 4, 2023, pp. 1886-1928.
DOI Link 2312
BibRef

Xu, C.[Chen], Xie, Y.[Yao],
Conformal Prediction for Time Series,
PAMI(45), No. 10, October 2023, pp. 11575-11587.
IEEE DOI 2310
BibRef

Sim, S.[Sunghyun], Kim, D.[Dohee], Bae, H.[Hyerim],
Correlation Recurrent Units: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data,
PAMI(45), No. 12, December 2023, pp. 14266-14283.
IEEE DOI 2311
BibRef

Giannoulis, M.[Michail], Harris, A.[Andrew], Barra, V.[Vincent],
DITAN: A deep-learning domain agnostic framework for detection and interpretation of temporally-based multivariate ANomalies,
PR(143), 2023, pp. 109814.
Elsevier DOI 2310
Multivariate time series, Anomaly detection, Neural networks, Generic normality feature learning, Predictability modeling BibRef

Sun, C.X.[Chen-Xi], Li, H.Y.[Hong-Yan], Song, M.[Moxian], Cai, D.[Derun], Zhang, B.F.[Bao-Feng], Hong, S.[Shenda],
Time pattern reconstruction for classification of irregularly sampled time series,
PR(147), 2024, pp. 110075.
Elsevier DOI 2312
Classification of irregularly sampled time series, Time pattern, Deep learning, Healthcare and medical application BibRef

Zhou, Z.[Zheng], Guo, X.[Xu], Xiong, Y.J.[Yu-Jie], Xia, C.M.[Chun-Ming],
Kalman-SSM: Modeling Long-Term Time Series With Kalman Filter Structured State Spaces,
SPLetters(31), 2024, pp. 2470-2474.
IEEE DOI 2410
Time series analysis, Kalman filters, Noise, Forecasting, Predictive models, Training, Convolution, Kalman filter, time series analysis BibRef

Wang, Z.[Zheng], Ran, H.W.[Hao-Wei], Ren, J.C.[Jin-Chang], Sun, M.J.[Mei-Jun],
PWDformer: Deformable transformer for long-term series forecasting,
PR(147), 2024, pp. 110118.
Elsevier DOI 2312
Long-term forecasting, Time series forecasting, Deep learning, Transformer BibRef

Jin, M.[Ming], Koh, H.Y.[Huan Yee], Wen, Q.S.[Qing-Song], Zambon, D.[Daniele], Alippi, C.[Cesare], Webb, G.I.[Geoffrey I.], King, I.[Irwin], Pan, S.R.[Shi-Rui],
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection,
PAMI(46), No. 12, December 2024, pp. 10466-10485.
IEEE DOI 2411
Time series analysis, Surveys, Task analysis, Graph neural networks, Forecasting, Imputation, Anomaly detection, anomaly detection BibRef

Jin, M.[Ming], Shi, G.[Guangsi], Li, Y.F.[Yuan-Fang], Xiong, B.[Bo], Zhou, T.[Tian], Salim, F.D.[Flora D.], Zhao, L.[Liang], Wu, L.F.[Ling-Fei], Wen, Q.S.[Qing-Song], Pan, S.R.[Shi-Rui],
Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting,
PAMI(47), No. 6, June 2025, pp. 4926-4939.
IEEE DOI 2505
Time series analysis, Forecasting, Graph neural networks, Convolution, Polynomials, Correlation, Australia, Training, deep learning BibRef

Campagner, A.[Andrea], Barandas, M.[Marília], Folgado, D.[Duarte], Gamboa, H.[Hugo], Cabitza, F.[Federico],
Ensemble Predictors: Possibilistic Combination of Conformal Predictors for Multivariate Time Series Classification,
PAMI(46), No. 11, November 2024, pp. 7205-7216.
IEEE DOI 2410
Time series analysis, Task analysis, Ensemble learning, Possibility theory, Focusing, Computational modeling, robustness BibRef

Cai, C.[Chao], Liu, W.[Weide], Xia, X.[Xue], Chen, Z.H.[Zheng-Hua], Fang, Y.M.[Yu-Ming],
Bayesian Uncertainty Calibration for Federated Time Series Analysis,
MultMed(26), 2024, pp. 11151-11163.
IEEE DOI 2412
Uncertainty, Time series analysis, Calibration, Predictive models, Task analysis, Data models, Training, Uncertainty calibration, federated learning BibRef

Sun, C.S.[Cong-Shan], Li, H.W.[Hong-Wei], Xu, C.[Cong], Ma, L.[Lin], Li, H.F.[Hai-Feng],
Adaptively Optimized Masking EMD for Separating Intrinsic Oscillatory Modes of Nonstationary Signals,
SPLetters(31), 2024, pp. 216-220.
IEEE DOI 2401
Empirical mode decomposition. BibRef

Li, Z.J.[Zi-Jian], Cai, R.[Ruichu], Fu, T.Z.J.[Tom Z. J.], Hao, Z.F.[Zhi-Feng], Zhang, K.[Kun],
Transferable Time-Series Forecasting Under Causal Conditional Shift,
PAMI(46), No. 4, April 2024, pp. 1932-1949.
IEEE DOI 2403
Forecasting, Adaptation models, Predictive models, Mathematical models, Data models, Time series analysis, semi-supervised domain adaptation BibRef

Yin, N.[Nan], Shen, L.[Li], Xiong, H.[Huan], Gu, B.[Bin], Chen, C.[Chong], Hua, X.S.[Xian-Sheng], Liu, S.W.[Si-Wei], Luo, X.[Xiao],
Messages are Never Propagated Alone: Collaborative Hypergraph Neural Network for Time-Series Forecasting,
PAMI(46), No. 4, April 2024, pp. 2333-2347.
IEEE DOI 2403
Time series analysis, Collaboration, Forecasting, Message passing, Correlation, Data models, Convolution, hypergraph neural network BibRef

Yang, L.[Luxuan], Gao, T.[Ting], Wei, W.[Wei], Dai, M.[Min], Fang, C.[Cheng], Duan, J.Q.[Jin-Qiao],
Multi-task meta label correction for time series prediction,
PR(150), 2024, pp. 110319.
Elsevier DOI 2403
Data visualization, Bi-level optimization, Meta-learning, Multi-task learning BibRef

Yu, Y.[Yang], Ma, R.Z.[Rui-Zhe], Ma, Z.M.[Zong-Min],
Robformer: A robust decomposition transformer for long-term time series forecasting,
PR(153), 2024, pp. 110552.
Elsevier DOI 2405
Long-term time series forecasting, Transformer, Time series decomposition BibRef

Liang, H.[Haobo], Feng, Y.[Yuan], Zhang, Y.S.[Yu-Shi], Qiao, X.[Xingshuai], Wang, Z.[Zhi], Shan, T.[Tao],
A Segmented Sliding Window Reference Signal Reconstruction Method Based on Fuzzy C-Means,
RS(16), No. 10, 2024, pp. 1813.
DOI Link 2405
BibRef

Zhan, T.X.[Tian-Xiang], Xiao, F.Y.[Fu-Yuan],
A novel weighted approach for time series forecasting based on visibility graph,
PR(155), 2024, pp. 110720.
Elsevier DOI 2408
Time series, Complex network, Visibility graph, Link forecasting BibRef

Li, C.H.[Chang-Hao], Ma, Z.X.[Zhi-Xin], Sun, D.[Dazhi], Zhang, G.M.[Guo-Ming], Wen, J.M.[Jin-Ming],
Stochastic IHT With Stochastic Polyak Step-Size for Sparse Signal Recovery,
SPLetters(31), 2024, pp. 2035-2039.
IEEE DOI 2408
Signal processing algorithms, Convergence, Vectors, Iterative algorithms, Stochastic processes, Sun, stochastic algorithms BibRef

Seddighi, Z.[Zahra], Taban, M.R.[Mohammad Reza], Gazor, S.[Saeed],
Cross-Terms and Spectral Leakage Minimization in Time-Frequency Distribution,
SPLetters(31), 2024, pp. 2025-2029.
IEEE DOI 2408
Kernel, Time-frequency analysis, Noise, Signal resolution, Discrete Fourier transforms, Estimation, Cross-term, quadratic, time-frequency distribution BibRef

Wen, L.J.[Liang-Jian], Hu, Q.[Quan], Guo, C.[Cong], Hu, A.[Ao], Zhang, M.Y.[Ming-Yi],
Cross-Scale Attention for Long-Term Time Series Forecasting,
SPLetters(31), 2024, pp. 2675-2679.
IEEE DOI 2410
Time series analysis, Forecasting, Transformers, Computational modeling, Biological system modeling, Semantics, cross-scale interaction BibRef

Zade, M.S.[Malihe Shojaee], Mesbah, M.[Mahmoud], Habibian, M.[Meeghat], Faroqi, H.[Hamed],
Converting Urban Trips to Multi-Dimensional Signals to Improve Trip Purpose Inference,
ITS(25), No. 10, October 2024, pp. 14497-14506.
IEEE DOI 2410
Smart cards, Public transportation, Time series analysis, Surveys, Signal processing algorithms, Probabilistic logic, Planning, trip purpose BibRef

Li, L.[Linzhi], Zhou, X.F.[Xiao-Feng], Hu, G.L.[Guo-Liang], Li, S.[Shuai], Jia, D.[Dongni],
A Recurrent Spatio-Temporal Graph Neural Network Based on Latent Time Graph for Multi-Channel Time Series Forecasting,
SPLetters(31), 2024, pp. 2875-2879.
IEEE DOI 2411
Time series analysis, Convolution, Predictive models, Forecasting, Correlation, Decoding, Mathematical models, Feature extraction, latent time graph BibRef

Ahishali, M.[Mete], Yamac, M.[Mehmet], Kiranyaz, S.[Serkan], Gabbouj, M.[Moncef],
Operational Support Estimator Networks,
PAMI(46), No. 12, December 2024, pp. 8442-8458.
IEEE DOI 2411
Inding the locations of non-zero elements in sparse signals. Task analysis, Estimation, Kernel, Neurons, Training, Dictionaries, Sparse approximation, Support estimation, sparse representation, machine learning BibRef

Yu, H.Q.[Hong-Qing], Wang, Z.[Ziyi], Qiao, H.[Heng],
On Variational Block Sparse Recovery With Unknown Partition and L_0-Norm Constraint,
SPLetters(31), 2024, pp. 96-100.
IEEE DOI 2401
BibRef

Rupniewski, M.W.[Marek W.],
Reconstruction of Recurring Pulses From Distribution of Short Sequences of Samples,
SPLetters(31), 2024, pp. 396-400.
IEEE DOI 2402
Probability distribution, Streams, Signal processing algorithms, Estimation, Approximation algorithms, Shape, nonuniform sampling BibRef

Zhang, D.D.[Dan-Dan], Zhang, Z.Q.[Zhi-Qiang], Chen, N.[Nanguang], Wang, Y.[Yun],
Dynamic convolutional time series forecasting based on adaptive temporal bilateral filtering,
PR(158), 2025, pp. 110985.
Elsevier DOI 2411
Nonlinear feature, Adaptive temporal bilateral filtering, Gated deformable convolution, Time series forecasting BibRef

Hou, M.J.[Ming-Jie], Liu, Z.Y.[Zhen-Yu], Sa, G.D.[Guo-Dong], Wang, Y.Y.[Yue-Yang], Sun, J.C.[Jia-Cheng], Li, Z.[Zhinan], Tan, J.R.[Jiang-Rong],
Parallel multi-scale dynamic graph neural network for multivariate time series forecasting,
PR(158), 2025, pp. 111037.
Elsevier DOI 2411
Multivariate time series forecasting, Parallel multi-scale dynamic modeling, Graph structure learning BibRef

Tan, G.[Gang], Wang, Y.Y.[Yue-Yang], Xiao, Z.[Ziyi], He, D.D.[Dan-Dan], Sa, G.D.[Guo-Dong],
From a multi-period perspective: A periodic dynamics forecasting network for multivariate time series forecasting,
PR(167), 2025, pp. 111760.
Elsevier DOI 2506
Multivariate time series forecasting, Graph neural networks, Graph structure learning, Deep learning BibRef

Morgenstern, G.[Gal], Routtenberg, T.[Tirza],
Theoretical Guarantees for Sparse Graph Signal Recovery,
SPLetters(32), 2025, pp. 266-270.
IEEE DOI 2501
Coherence, Sparse matrices, Laplace equations, Dictionaries, Atoms, Filtering theory, Upper bound, Sensors, Matrices, sparse recovery BibRef

Wang, C.Y.[Chao-Yi], Gao, J.[Jinchun], Song, K.X.[Kai-Xuan], Wang, W.J.[Wen-Jia], Chen, Z.J.[Zhi-Jiao],
Novel Mathematical Model of Voltage Waveform Prediction for High-Speed PRBS Signal Transmission,
SPLetters(32), 2025, pp. 376-380.
IEEE DOI 2501
Mathematical models, Integrated circuit interconnections, Time-domain analysis, Integrated circuit modeling, time-domain waveform BibRef

Reddy, S.[Siddartha], Chepuri, S.P.[Sundeep Prabhakar],
Recovery of Signals on a Simplicial Complex From Subsampled Neighborhood Aggregations,
SPLetters(32), 2025, pp. 536-540.
IEEE DOI 2501
Laplace equations, Vectors, Matrix decomposition, Eigenvalues and eigenfunctions, Biological system modeling, least squares recovery BibRef

Qian, S.[Siyu], Xue, Z.H.[Zhao-Hui], Jia, M.M.[Ming-Ming], Zhang, H.S.[Hong-Sheng],
Streamlined multilayer perceptron for contaminated time series reconstruction: A case study in coastal zones of southern China,
PandRS(221), 2025, pp. 193-209.
Elsevier DOI 2503
Time series reconstruction, Coastal zones, Multilayer perceptron, Frequency principle theory, Implicit neural representation BibRef

Luo, Y.X.[Yu-Xiao], Zhang, S.[Songming], Lyu, Z.Y.[Zi-Yu], Hu, Y.H.[Yu-Han],
TFDNet: Time-Frequency enhanced Decomposed Network for long-term time series forecasting,
PR(162), 2025, pp. 111412.
Elsevier DOI Code:
WWW Link. 2503
Time series forecasting, Channel-wise effect, Time-frequency domain BibRef

Troncoso-García, Á.R.[Ángela R.], Martínez-Ballesteros, M.[María], Martínez-Álvarez, F.[Francisco], Troncoso, A.[Alicia],
A New Metric Based on Association Rules to Assess Feature-Attribution Explainability Techniques for Time Series Forecasting,
PAMI(47), No. 5, May 2025, pp. 4140-4155.
IEEE DOI 2504
Measurement, Time series analysis, Explainable AI, Artificial intelligence, Predictive models, Forecasting, time series forecasting BibRef

Chen, J.W.[Jia-Wei], Zhao, C.H.[Chun-Hui],
Addressing Information Asymmetry: Deep Temporal Causality Discovery for Mixed Time Series,
PAMI(47), No. 7, July 2025, pp. 5723-5741.
IEEE DOI 2506
Voltage control, Time series analysis, Cause effect analysis, Training, Kernel, Data models, Correlation, Optimization, self-supervised learning BibRef

Hao, H.C.[Hong-Chi], Ren, S.[Shouyu], Li, H.[Hanru], Yu, Z.B.[Zhi-Bin],
Selective Enhanced Attention and Cross-Component Interaction for Long-Term Time Series Forecasting,
SPLetters(32), 2025, pp. 2264-2268.
IEEE DOI 2506
Time series analysis, Correlation, Forecasting, Transformers, Noise, Training, Overfitting, Predictive models, Market research, cross-component interaction BibRef

Itzhak, N.[Nevo], Jaroszewicz, S.[Szymon], Moskovitch, R.[Robert],
Time-intervals-related pattern selection for continuous event prediction,
PR(168), 2025, pp. 111546.
Elsevier DOI 2506
Temporal pattern selection, Real-time prediction, Multivariate time-series BibRef

Huang, D.X.[Dong-Xuan], Lv, X.F.[Xing-Feng], Zhang, Y.[Yang],
MSNet: Multi-task self-supervised network for time series classification,
PRL(191), 2025, pp. 73-79.
Elsevier DOI 2504
Deep learning, Time series classification, Self-supervised learning, Contrastive learning, Signal reconstruction BibRef

de Boer, F.[Frans], van Gemert, J.C.[Jan C.], Dijkstra, J.[Jouke], Pintea, S.L.[Silvia L.],
Is there progress in activity progress prediction?,
CVEU23(2950-2958)
IEEE DOI 2401
How much time remains kind of questions. BibRef

Chen, Y.J.[Ya-Juan], Li, Z.L.[Zhan-Li], Li, H.A.[Hong-An], Yang, S.[Sa],
Attention-GRU Based Method for Predicting Coal Mine Water Surge Analysis,
ICIVC22(913-920)
IEEE DOI 2301
Training, Measurement, Computational modeling, Neural networks, Predictive models, Prediction algorithms, Data models, gated recurrent unit neural networks BibRef

Zhang, Z.Z.[Zhao-Zhao], Wang, X.H.[Xiao-Hui], Zhu, Y.Q.[Ying-Qin],
Echo State Network Optimization Based on Improved Fireworks Algorithm,
ICIVC22(854-859)
IEEE DOI 2301
Fireworks algorithm, Analytical models, Computational modeling, Time series analysis, Predictive models, Benchmark testing, echo state network BibRef

Khryashchev, D.A.[Denis A.], Vo, H.T.[Huy T.], Haralick, R.M.[Robert M.],
Partial Monotone Dependence,
ICPR21(6375-6382)
IEEE DOI 2105
Correlation coefficient, Cellular networks, Correlation, Time series analysis, Stochastic processes, Predictive models, Time measurement BibRef

Ojeda, C.[César], Georgiev, B.[Bogdan], Cvejoski, K.[Kostadin], Schucker, J.[Jannis], Bauckhage, C.[Christian], Sánchez, R.J.[Ramsés J.],
Switching Dynamical Systems with Deep Neural Networks,
ICPR21(6305-6312)
IEEE DOI 2105
Time series analysis, Neural networks, Switches, Predictive models, Data models, Dynamical systems BibRef

Holmgren, K.[Kimberly], Gibby, P.[Paul], Zipkin, J.R.[Joseph R.],
Seasonal Inhomogeneous Nonconsecutive Arrival Process Search and Evaluation,
ICPR21(1282-1289)
IEEE DOI 2105
Resistance, Time series analysis, Fitting, Training data, Predictive models, Nonhomogeneous media, Data models BibRef

Kim, J.H.[Jin-Hee], Kim, T.[Taesung], Choi, J.H.[Jang-Ho], Choo, J.[Jaegul],
End-to-end Multi-task Learning of Missing Value Imputation and Forecasting in Time-Series Data,
ICPR21(8849-8856)
IEEE DOI 2105
Training, Noise reduction, Predictive models, Logic gates, Data models, Sensors BibRef

Camastra, F.[Francesco], Capone, V.[Vincenzo], Ciaramella, A.[Angelo], Landi, T.C.[Tony Christian], Riccio, A.[Angelo], Staiano, A.[Antonino],
Environmental Time Series Prediction with Missing Data by Machine Learning and Dynamics Recostruction,
MAES20(26-33).
Springer DOI 2103
BibRef

Wang, Z.Y.[Zi-Yin], Tsechpenakis, G.[Gavriil],
Stream Clustering with Dynamic Estimation of Emerging Local Densities,
ICPR18(2100-2105)
IEEE DOI 1812
Kernel, Clustering algorithms, Dictionaries, Approximation algorithms, Estimation, Complexity theory, Testing BibRef

Jiang, L.[Li], Zhou, J.[Junni], Yang, R.L.[Run-Ling], Liu, L.[Li], Li, L.[Lin],
Parameter estimation of LFMCW signal using S-Method with adaptive window,
ICIVC17(875-878)
IEEE DOI 1708
Estimation, Frequency estimation, Frequency modulation, Signal to noise ratio, Time-frequency analysis, Transforms, S-Method, adaptive window, linear frequency modulated continuous wave, short-time fourier transform, time frequency analysis. BibRef