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

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Showing 1551–1575 of 6748 papers

TitleStatusHype
Evaluating Impact of Social Media Posts by Executives on Stock PricesCode0
Covariate-guided Bayesian mixture model for multivariate time seriesCode0
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series ClassificationCode0
Multi-view Integration Learning for Irregularly-sampled Clinical Time SeriesCode0
Evaluating data augmentation for financial time series classificationCode0
EuroGames16: Evaluating Change Detection in Online ConversationCode0
COVID-19 epidemiology as emergent behavior on a dynamic transmission forestCode0
A Subspace Method for Time Series Anomaly Detection in Cyber-Physical SystemsCode0
Evaluating Explanation Methods for Multivariate Time Series ClassificationCode0
Change of human mobility during COVID-19: A United States case studyCode0
AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous SensorsCode0
Nested Multiple Instance Learning with Attention MechanismsCode0
Evaluating generation of chaotic time series by convolutional generative adversarial networksCode0
Experimental Study on Time Series Analysis of Lower Limb Rehabilitation Exercise Data Driven by Novel Model Architecture and Large ModelsCode0
Challenges in detecting evolutionary forces in language change using diachronic corporaCode0
Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noiseCode0
Estimating optical vegetation indices and biophysical variables for temperate forests with Sentinel-1 SAR data using machine learning techniques: A case study for CzechiaCode0
Estimating Vector Fields from Noisy Time SeriesCode0
Estimation of Large Covariance and Precision Matrices from Temporally Dependent ObservationsCode0
eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart ChairCode0
Central object segmentation by deep learning for fruits and other roundish objectsCode0
Estimating activity cycles with probabilistic methods I. Bayesian Generalised Lomb-Scargle Periodogram with TrendCode0
Community recovery in non-binary and temporal stochastic block modelsCode0
Ensemble Sales Forecasting Study in Semiconductor IndustryCode0
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational CostsCode0
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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