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 5326–5350 of 6748 papers

TitleStatusHype
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables—0
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series DataCode0
Unsupervised Learning in Reservoir Computing for EEG-based Emotion Recognition—0
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal DynamicsCode0
Transform-Based Multilinear Dynamical System for Tensor Time Series Analysis—0
Real-time Power System State Estimation and Forecasting via Deep Neural NetworksCode0
Short-Term Wind-Speed Forecasting Using Kernel Spectral Hidden Markov Models—0
Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior—0
Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting—0
Structural Damage Detection and Localization with Unknown Post-Damage Feature Distribution Using Sequential Change-Point Detection Method—0
Adversarial Unsupervised Representation Learning for Activity Time-Series—0
Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection—0
Fast Distribution Grid Line Outage Identification with μPMU—0
Reduced-order modeling with artificial neurons for gravitational-wave inferenceCode0
The Altes Family of Log-Periodic Chirplets and the Hyperbolic Chirplet Transform—0
Assessing biological models using topological data analysis—0
Learning Representations of Missing Data for Predicting Patient Outcomes—0
Langevin-gradient parallel tempering for Bayesian neural learningCode0
StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures—0
EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction—0
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flowCode0
Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series—0
Time Series Classification to Improve Poultry Welfare—0
Estimating Network Structure from Incomplete Event Data—0
Day-ahead time series forecasting: application to capacity planning—0
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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