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 3601–3625 of 6748 papers

TitleStatusHype
Forecasting Short-term load using Econometrics time series model with T-student Distribution—0
Anomaly Detection and Sampling Cost Control via Hierarchical GANs—0
Inferring Global Dynamics Using a Learning Machine—0
Instance-based Counterfactual Explanations for Time Series ClassificationCode1
Semi-Supervised Learning for In-Game Expert-Level Music-to-Dance Translation—0
Piece-wise Matching Layer in Representation Learning for ECG Classification—0
Decision-Aware Conditional GANs for Time Series Data—0
A first econometric analysis of the CRIX family—0
Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data—0
A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting—0
Deep Learning based Covert Attack Identification for Industrial Control Systems—0
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
Cloud Cover Nowcasting with Deep Learning—0
N-BEATS neural network for mid-term electricity load forecastingCode0
Limit Theorems for Factor Models—0
A Linear Transportation L^p Distance for Pattern Recognition—0
CoVaR with volatility clustering, heavy tails and non-linear dependence—0
Vertical Power Flow Forecast with LSTMs Using Regular Training Update Strategies—0
Breaking Symmetries of the Reservoir Equations in Echo State Networks—0
A Time Series Data Analysis of Indian Commercial Dynamism—0
Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: a semantic segmentation solution—0
Survey of explainable machine learning with visual and granular methods beyond quasi-explanations—0
Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage NormalizationCode0
Latent State Inference in a Spatiotemporal Generative Model—0
From Static to Dynamic Node Embeddings—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