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 176–200 of 6748 papers

TitleStatusHype
COT-GAN: Generating Sequential Data via Causal Optimal TransportCode1
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of ProgressCode1
Deep ConvLSTM with self-attention for human activity decoding using wearablesCode1
Conformal Time-series ForecastingCode1
An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural NetworksCode1
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural NetworksCode1
The Signature Kernel is the solution of a Goursat PDECode1
An Experimental Review on Deep Learning Architectures for Time Series ForecastingCode1
Conditional GAN for timeseries generationCode1
An Evaluation of Change Point Detection AlgorithmsCode1
Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategyCode1
Conditional Sig-Wasserstein GANs for Time Series GenerationCode1
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking ApplicationsCode1
A Deep Learning Approach to Analyzing Continuous-Time SystemsCode1
Conformal prediction set for time-seriesCode1
Anomaly Detection of Wind Turbine Time Series using Variational Recurrent AutoencodersCode1
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time seriesCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
ASTRIDE: Adaptive Symbolization for Time Series DatabasesCode1
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-SeriesCode1
Contrastive Learning for Unsupervised Domain Adaptation of Time SeriesCode1
Neural graphical modelling in continuous-time: consistency guarantees and algorithmsCode1
An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series ForecastingCode1
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
A Neural PDE Solver with Temporal Stencil ModelingCode1
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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