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 4751–4775 of 6748 papers

TitleStatusHype
Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team ScenarioCode0
Independence Testing for Temporal Data—0
eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart ChairCode0
Comprehensive Time-Series Regression Models Using GRETL -- U.S. GDP and Government Consumption Expenditures & Gross Investment from 1980 to 2013—0
Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks—0
Locally Linear Embedding and fMRI feature selection in psychiatric classification—0
HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs—0
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded EmbeddingCode0
A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style—0
Mixed pooling of seasonality for time series forecasting: An application to pallet transport data—0
Towards automated symptoms assessment in mental health—0
MEx: Multi-modal Exercises Dataset for Human Activity RecognitionCode0
Local Score Dependent Model Explanation for Time Dependent Covariates—0
Wasserstein Index Generation Model: Automatic Generation of Time-series Index with Application to Economic Policy UncertaintyCode0
Mixture-based Multiple Imputation Model for Clinical Data with a Temporal DimensionCode0
Learning towards Abstractive Timeline SummarizationCode0
E2GAN: End-to-End Generative Adversarial Network or Multivariate Time Series Imputation—0
Autoregressive-Model-Based Methods for Online Time Series Prediction with Missing Values: an Experimental Evaluation—0
DeepAISE -- An End-to-End Development and Deployment of a Recurrent Neural Survival Model for Early Prediction of Sepsis—0
Show Me Your Account: Detecting MMORPG Game Bot Leveraging Financial Analysis with LSTM—0
A persistent homology approach to heart rate variability analysis with an application to sleep-wake classificationCode0
LSTM-based Flow Prediction—0
TEASER: Early and Accurate Time Series ClassificationCode0
Modeling Extreme Events in Time Series Prediction—0
Identification of Effective Connectivity Subregions—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