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 2501–2525 of 6748 papers

TitleStatusHype
Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles—0
A CNN based method for Sub-pixel Urban Land Cover Classification using Landsat-5 TM and Resourcesat-1 LISS-IV Imagery—0
Leveraging Image-based Generative Adversarial Networks for Time Series Generation—0
Optimal Latent Space Forecasting for Large Collections of Short Time Series Using Temporal Matrix Factorization—0
A Predictive Online Transient Stability Assessment with Hierarchical Generative Adversarial Networks—0
Characterization of causal ancestral graphs for time series with latent confounders—0
Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data—0
Non-iterative Calculation of Quasi-Dynamic Energy Flow in the Heat and Electricity Integrated Energy SystemsCode0
Compensatory model for quantile estimation and application to VaR—0
Cryptocurrency Market Consolidation in 2020--2021—0
DeepFIB: Self-Imputation for Time Series Anomaly Detection—0
Approximation algorithms for confidence bands for time series—0
Federated Reinforcement Learning at the Edge—0
Mathematical models of COVID-19 spread—0
Neural Multi-Quantile Forecasting for Optimal Inventory Management—0
Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction—0
Autoregressive Quantile Flows for Predictive Uncertainty Estimation—0
Measuring Wind Turbine Health Using Drifting Concepts—0
Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection—0
Merging Subject Matter Expertise and Deep Convolutional Neural Network for State-Based Online Machine-Part Interaction Classification—0
Differentially Private K-means Clustering Applied to Meter Data Analysis and Synthesis—0
Change of persistence in European electricity spot prices—0
Online false discovery rate control for anomaly detection in time series—0
Optimal activity and battery scheduling algorithm using load and solar generation forecastCode0
Learning Generalized Causal Structure in Time-series—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