SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5726–5750 of 6748 papers

TitleStatusHype
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence ModelCode0
Neural Dynamics Discovery via Gaussian Process Recurrent Neural NetworksCode0
Sparsity-Assisted Signal Denoising and Pattern Recognition in Time-Series DataCode0
Dynamic process fault prediction using canonical variable trend analysisCode0
RecovDB: accurate and efficient missing blocks recovery for large time seriesCode0
Dynamic Natural Language Processing with Recurrence Quantification AnalysisCode0
Imbedding Deep Neural NetworksCode0
Approximate Factor Models for Functional Time SeriesCode0
Time Series Source Separation using Dynamic Mode DecompositionCode0
Neural Granger CausalityCode0
Neural Inference of Gaussian Processes for Time Series Data of QuasarsCode0
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change SegmentationCode0
Dynamic cyber risk estimation with Competitive Quantile AutoregressionCode0
Recovering lost and absent information in temporal networksCode0
Quantum open system identification via global optimization: Optimally accurate Markovian models of open systems from time-series dataCode0
Tree Echo State Autoencoders with GrammarsCode0
Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning ApproachCode0
Recovery of Future Data via Convolution Nuclear Norm MinimizationCode0
Weak error rates for option pricing under linear rough volatilityCode0
DynaConF: Dynamic Forecasting of Non-Stationary Time SeriesCode0
DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural NetworksCode0
DTW-Merge: A Novel Data Augmentation Technique for Time Series ClassificationCode0
DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series AnalysisCode0
Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time SeriesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified