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 2051–2075 of 6748 papers

TitleStatusHype
Self-supervised Autoregressive Domain Adaptation for Time Series DataCode1
TsFeX: Contact Tracing Model using Time Series Feature Extraction and Gradient Boosting—0
Deep Decomposition for Stochastic Normal-Abnormal Transport—0
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series ClassificationCode0
Harnessing expressive capacity of Machine Learning modeling to represent complex coupling of Earth's auroral space weather regimes—0
NeuralProphet: Explainable Forecasting at ScaleCode2
Improving Deep Learning Interpretability by Saliency Guided TrainingCode1
Enhancing Identification of Structure Function of Academic Articles Using Contextual InformationCode0
Learning Wildfire Model from Incomplete State Observations—0
Learning Physical Concepts in Cyber-Physical Systems: A Case StudyCode0
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection—0
Achieving an Accurate Random Process Model for PV Power using Cheap Data: Leveraging the SDE and Public Weather Reports—0
Factor-augmented tree ensemblesCode0
Correlation Based Feature Subset Selection for Multivariate Time-Series Data—0
SARS-CoV-2 Dissemination using a Network of the United States Counties—0
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors—0
Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time WarpingCode1
Wavelet‐attention‐based traffic prediction for smart cities—0
Expert Aggregation for Financial Forecasting—0
A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles—0
Neural network stochastic differential equation models with applications to financial data forecastingCode1
Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time SeriesCode0
Animal Behavior Classification via Accelerometry Data and Recurrent Neural Networks—0
Animal behavior classification via deep learning on embedded systems—0
tsflex: flexible time series processing & feature extractionCode1
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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