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 851–875 of 6748 papers

TitleStatusHype
Attention to Warp: Deep Metric Learning for Multivariate Time SeriesCode1
Fast Variational Learning in State-Space Gaussian Process ModelsCode1
Attentive Neural Controlled Differential Equations for Time-series Classification and ForecastingCode1
An efficient aggregation method for the symbolic representation of temporal dataCode1
FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic ForecastingCode1
Feature-Based Time-Series Analysis in R using the theft PackageCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
Temporal Dependencies in Feature Importance for Time Series PredictionsCode1
FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series ForecastingCode1
Federated Foundation Models on Heterogeneous Time SeriesCode1
FedMood: Federated Learning on Mobile Health Data for Mood DetectionCode1
Temporal Phenotyping using Deep Predictive Clustering of Disease ProgressionCode1
FedTADBench: Federated Time-Series Anomaly Detection BenchmarkCode1
Finding active galactic nuclei through FinkCode1
Few-Shot Forecasting of Time-Series with Heterogeneous ChannelsCode1
Ti-MAE: Self-Supervised Masked Time Series AutoencodersCode1
Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering AlgorithmCode1
Few-Shot One-Class Classification via Meta-LearningCode1
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence ModelingCode1
Financial Time Series Data Processing for Machine LearningCode1
HIVE-COTE 2.0: a new meta ensemble for time series classificationCode1
Combating Distribution Shift for Accurate Time Series Forecasting via HypernetworksCode1
Instance-based Counterfactual Explanations for Time Series ClassificationCode1
First De-Trend then Attend: Rethinking Attention for Time-Series ForecastingCode1
Long Short-Term Memory Spiking Networks and Their ApplicationsCode1
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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