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 5701–5725 of 6748 papers

TitleStatusHype
Impact of non-stationarity on estimating and modeling empirical copulas of daily stock returnsCode0
Network Inference via the Time-Varying Graphical LassoCode0
Anomaly detection in the dynamics of web and social networksCode0
Edge computing on TPU for brain implant signal analysisCode0
Economy Statistical Recurrent Units For Inferring Nonlinear Granger CausalityCode0
Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS DiagnosisCode0
Spikebench: An open benchmark for spike train time-series classificationCode0
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction ModelsCode0
EasyMLServe: Easy Deployment of REST Machine Learning ServicesCode0
Anomaly detection in dynamic networksCode0
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health EpisodesCode0
Gradient Importance Learning for Incomplete ObservationsCode0
Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State NetworkCode0
Early Abandoning PrunedDTW and its application to similarity searchCode0
imputeTS: Time Series Missing Value Imputation in RCode0
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision MakingCode0
Neural circuits for dynamics-based segmentation of time seriesCode0
Reconstructing shared dynamics with a deep neural networkCode0
Incorporating Stock Market Signals for Twitter Stance DetectionCode0
Dynamic Virtual Graph Significance Networks for Predicting InfluenzaCode0
Dynamic transformation of prior knowledge into Bayesian models for data streamsCode0
Data-Driven Copy-Paste Imputation for Energy Time SeriesCode0
Dynamic Time Warping based Adversarial Framework for Time-Series DomainCode0
Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine LearningCode0
Reconstructing large networks with time-varying interactionsCode0
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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