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 3551–3575 of 6748 papers

TitleStatusHype
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control—0
Deep Reservoir Networks with Learned Hidden Reservoir Weights using Direct Feedback Alignment—0
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling—0
Early Abandoning PrunedDTW and its application to similarity searchCode0
A Case-Study on the Impact of Dynamic Time Warping in Time Series Regression—0
Interpretable Neural Networks for Panel Data Analysis in Economics—0
Explainable Framework for Time-series Analysis via Topological Data Analysis—0
Bifurcation Analysis using Zigzag Persistence—0
Downsampling and geometric feature methods for EEG classification tasks with CNNs—0
Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges—0
Asset Price Forecasting using Recurrent Neural NetworksCode0
Structured Self-Attention Weights Encode Semantics in Sentiment AnalysisCode0
TOTOPO: Classifying univariate and multivariate time series with Topological Data Analysis—0
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
A Predictive Autoscaler for Elastic Batch Jobs—0
Deep Reinforcement Learning for Asset Allocation in US Equities—0
Spatio-Temporal Stability Analysis in Satellite Image Times SeriesCode0
HydroDeep -- A Knowledge Guided Deep Neural Network for Geo-Spatiotemporal Data Analysis—0
Recurrent convolutional neural network for the surrogate modeling of subsurface flow simulation—0
The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments and a Paradox Concerning Logging Policy—0
Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems—0
MIA-Prognosis: A Deep Learning Framework to Predict Therapy ResponseCode0
Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning—0
Multivariate Temporal Autoencoder for Predictive Reconstruction of Deep Sequences—0
A Transformer-based Framework for Multivariate Time Series Representation LearningCode1
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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