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

Showing 13261350 of 6748 papers

TitleStatusHype
Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variablesCode0
fETSmcs: Feature-based ETS model component selectionCode0
Targeted stochastic gradient Markov chain Monte Carlo for hidden Markov models with rare latent statesCode0
Feature space approximation for kernel-based supervised learningCode0
Feature Selection for Multivariate Time Series via Network PruningCode0
Feature engineering workflow for activity recognition from synchronized inertial measurement unitsCode0
Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 MethodsCode0
Clustering Based Feature Learning on Variable StarsCode0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
Applying Machine Learning to Crowd-sourced Data from Earthquake DetectiveCode0
Fast Online Deconvolution of Calcium Imaging DataCode0
Fast ES-RNN: A GPU Implementation of the ES-RNN AlgorithmCode0
Combining datasets to increase the number of samples and improve model fittingCode0
Faster Retrieval with a Two-Pass Dynamic-Time-Warping Lower BoundCode0
Generating Sparse Counterfactual Explanations For Multivariate Time SeriesCode0
Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time series dataCode0
Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series ClassifiersCode0
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEsCode0
Few-shot human motion prediction for heterogeneous sensorsCode0
Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian ApproachCode0
Global Models for Time Series Forecasting: A Simulation StudyCode0
Human Activity Recognition using Multi-Head CNN followed by LSTMCode0
Granger Causality using Neural NetworksCode0
Apply Artificial Neural Network to Solving Manpower Scheduling ProblemCode0
Extracting Relationships by Multi-Domain MatchingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified