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 2551–2575 of 6748 papers

TitleStatusHype
Deep Decomposition for Stochastic Normal-Abnormal Transport—0
TsFeX: Contact Tracing Model using Time Series Feature Extraction and Gradient Boosting—0
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series ClassificationCode0
Changepoint Analysis of Topic Proportions in Temporal Text Data—0
Harnessing expressive capacity of Machine Learning modeling to represent complex coupling of Earth's auroral space weather regimes—0
Learning Physical Concepts in Cyber-Physical Systems: A Case StudyCode0
Enhancing Identification of Structure Function of Academic Articles Using Contextual InformationCode0
Learning Wildfire Model from Incomplete State Observations—0
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
SARS-CoV-2 Dissemination using a Network of the United States Counties—0
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors—0
Correlation Based Feature Subset Selection for Multivariate Time-Series Data—0
A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles—0
Wavelet‐attention‐based traffic prediction for smart cities—0
Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time SeriesCode0
Expert Aggregation for Financial Forecasting—0
Animal behavior classification via deep learning on embedded systems—0
Animal Behavior Classification via Accelerometry Data and Recurrent Neural Networks—0
Causal Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemic—0
Light-weight Gesture Sensing Using FMCW Radar Time Series Data—0
Time Series Prediction about Air Quality using LSTM-Based Models: A Systematic Mapping—0
4D iterative reconstruction of brain fMRI in the moving fetus—0
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version—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