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 251–275 of 6748 papers

TitleStatusHype
Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series AnalysisCode1
Benchmarking Deep Learning Interpretability in Time Series PredictionsCode1
Early Abandoning and Pruning for Elastic Distances including Dynamic Time WarpingCode1
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signalsCode1
Deep Adaptive Input Normalization for Time Series ForecastingCode1
Deep Dynamic Factor ModelsCode1
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19Code1
Domain Adaptation for Time Series Forecasting via Attention SharingCode1
Self-Supervised Time Series Representation Learning via Cross Reconstruction TransformerCode1
Crop mapping from image time series: deep learning with multi-scale label hierarchiesCode1
AA-Forecast: Anomaly-Aware Forecast for Extreme EventsCode1
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time SeriesCode1
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series ImputationCode1
An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series ForecastingCode1
COVID-19 Data Analysis and Forecasting: Algeria and the WorldCode1
CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact DataCode1
An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly DetectionCode1
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential EquationsCode1
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of ProgressCode1
Anomaly Detection of Wind Turbine Time Series using Variational Recurrent AutoencodersCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
COT-GAN: Generating Sequential Data via Causal Optimal TransportCode1
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal BootstrappingCode1
Cost-effective Interactive Attention Learning with Neural Attention ProcessesCode1
Contrastive Domain Adaptation for Time-Series via Temporal MixupCode1
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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