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

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Showing 701–725 of 6748 papers

TitleStatusHype
Deep Adaptive Input Normalization for Time Series ForecastingCode1
First De-Trend then Attend: Rethinking Attention for Time-Series ForecastingCode1
Forecasting with Deep LearningCode1
A Synthetic Texas Power System with Time-Series Weather-Dependent Spatiotemporal ProfilesCode1
Multi-Time Attention Networks for Irregularly Sampled Time SeriesCode1
Deep Counterfactual Estimation with Categorical Background VariablesCode1
Deep Dynamic Factor ModelsCode1
Multivariate Time-series Anomaly Detection via Graph Attention NetworkCode1
Deep Explicit Duration Switching Models for Time SeriesCode1
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series DataCode1
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural NetworksCode1
Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic DataCode1
FedTADBench: Federated Time-Series Anomaly Detection BenchmarkCode1
NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series ForecastingCode1
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series DataCode1
Deep Latent State Space Models for Time-Series GenerationCode1
Network Traffic Classification based on Single Flow Time Series AnalysisCode1
Deep Isolation Forest for Anomaly DetectionCode1
Deep Learning-based Damage Mapping with InSAR Coherence Time SeriesCode1
Deep Learning for Time Series Anomaly Detection: A SurveyCode1
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vesselsCode1
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningCode1
Federated Learning for 5G Base Station Traffic ForecastingCode1
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning RulesCode1
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data DetectionCode1
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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