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 6451–6475 of 6748 papers

TitleStatusHype
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing DataCode0
A Multi-Horizon Quantile Recurrent ForecasterCode0
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural NetworksCode0
Clustering Market Regimes using the Wasserstein DistanceCode0
Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific FeaturesCode0
Model Agnostic Time Series Analysis via Matrix EstimationCode0
Semi-supervised Sequence Modeling for Elastic Impedance InversionCode0
Targeted stochastic gradient Markov chain Monte Carlo for hidden Markov models with rare latent statesCode0
Clustering Based Feature Learning on Variable StarsCode0
Set Functions for Time SeriesCode0
YASS: Yet Another Spike SorterCode0
Prediction of Landfall Intensity, Location, and Time of a Tropical CycloneCode0
Model Compression for Dynamic Forecast CombinationCode0
Shallow RNN: Accurate Time-series Classification on Resource Constrained DevicesCode0
Class-Specific Explainability for Deep Time Series ClassifiersCode0
Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series ForecastCode0
ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter AveragingCode0
Trading via Image ClassificationCode0
shapeDTW: shape Dynamic Time WarpingCode0
Shapelet-Based Counterfactual Explanations for Multivariate Time SeriesCode0
Task-oriented Time Series Imputation Evaluation via Generalized RepresentersCode0
Predictive Auto-scaling with OpenStack MonascaCode0
Structured Recognition for Generative Models with Explaining AwayCode0
A persistent homology approach to heart rate variability analysis with an application to sleep-wake classificationCode0
Time Series Data Cleaning: From Anomaly Detection to Anomaly RepairingCode0
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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