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 1551–1575 of 6748 papers

TitleStatusHype
Covariance shrinkage for autocorrelated data—0
A Study of Joint Graph Inference and Forecasting—0
CoVaR with volatility clustering, heavy tails and non-linear dependence—0
Modeling Macroeconomic Variations After COVID-19—0
A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow—0
Conditional Generative Adversarial Networks to Model Urban Outdoor Air Pollution—0
Time Series Analysis and Modeling to Forecast: a Survey—0
COVID-19 Hospitalizations Forecasts Using Internet Search Data—0
Covid-19 impact on cryptocurrencies: evidence from a wavelet-based Hurst exponent—0
COVID-19 infection and recovery in various countries: Modeling the dynamics and evaluating the non-pharmaceutical mitigation scenarios—0
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics—0
COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining—0
COVID-19 forecasting using new viral variants and vaccination effectiveness models—0
COVID-19: Tail Risk and Predictive Regressions—0
AAMDRL: Augmented Asset Management with Deep Reinforcement Learning—0
COVID-19: The extraction of the effective reproduction number from the time series of new cases—0
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide—0
CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution—0
Creating cloud-free satellite imagery from image time series with deep learning—0
Creating Disasters: Recession Forecasting with GAN-Generated Synthetic Time Series Data—0
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information—0
A Critical Overview of Privacy-Preserving Approaches for Collaborative Forecasting—0
Critical Transitions in Intensive Care Units: A Sepsis Case Study—0
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition—0
Learning low-dimensional state embeddings and metastable clusters from time series data—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