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 1801–1825 of 6748 papers

TitleStatusHype
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change SegmentationCode0
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
Predicting Sparse Clients' Actions with CPOPT-Net in the Banking EnvironmentCode0
SSIM -A Deep Learning Approach for Recovering Missing Time Series Sensor DataCode0
Building Effective Large-Scale Traffic State Prediction System: Traffic4cast Challenge SolutionCode0
Standing on the Shoulders of Machine Learning: Can We Improve Hypothesis Testing?Code0
DynaConF: Dynamic Forecasting of Non-Stationary Time SeriesCode0
Dimensionless Anomaly Detection on Multivariate Streams with Variance Norm and Path SignatureCode0
DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural NetworksCode0
DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series AnalysisCode0
A Complex Systems Approach To Feature Extraction for Chaotic Behavior RecognitionCode0
Stochastic embeddings of dynamical phenomena through variational autoencodersCode0
Dropout Feature Ranking for Deep Learning ModelsCode0
Deep Learning MacroeconomicsCode0
A genetic algorithm to discover flexible motifs with supportCode0
DSANet: Dual Self-Attention Network for Multivariate Time Series ForecastingCode0
DTW-Merge: A Novel Data Augmentation Technique for Time Series ClassificationCode0
Efficient learning of nonlinear prediction models with time-series privileged informationCode0
Evaluating Explanation Methods for Multivariate Time Series ClassificationCode0
Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural NetworksCode0
BRITS: Bidirectional Recurrent Imputation for Time SeriesCode0
Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time SeriesCode0
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signalCode0
Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series PredictionCode0
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity RecognitionCode0
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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