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 2651–2675 of 6748 papers

TitleStatusHype
COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining—0
COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms—0
COVID-19 infection and recovery in various countries: Modeling the dynamics and evaluating the non-pharmaceutical mitigation scenarios—0
Covid-19 impact on cryptocurrencies: evidence from a wavelet-based Hurst exponent—0
A Survey of Feature Types and Their Contributions for Camera Tampering Detection—0
Analysis of Advisor Portfolio using Multivariate Time Series and Cosine Similarity—0
A Deep-Learning Based Optimization Approach to Address Stop-Skipping Strategy in Urban Rail Transit Lines—0
COVID-19 Hospitalizations Forecasts Using Internet Search Data—0
A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning—0
Time Series Analysis and Modeling to Forecast: a Survey—0
Modeling Macroeconomic Variations After COVID-19—0
CoVaR with volatility clustering, heavy tails and non-linear dependence—0
A study of the Multicriteria decision analysis based on the time-series features and a TOPSIS method proposal for a tensorial approach—0
Covariance shrinkage for autocorrelated data—0
A Study of Joint Graph Inference and Forecasting—0
Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models—0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
Covariance-engaged Classification of Sets via Linear Programming—0
Coupled Recurrent Models for Polyphonic Music Composition—0
COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction—0
A Study of Aggregation of Long Time-series Input for LSTM Neural Networks—0
Analysis and development of an automatic eCall for motorcycles: a one-class cepstrum approach—0
Towards Explainable Land Cover Mapping: a Counterfactual-based Strategy—0
CoughTrigger: Earbuds IMU Based Cough Detection Activator Using An Energy-efficient Sensitivity-prioritized Time Series Classifier—0
Cough Detection Using Hidden Markov Models—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