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 4001–4025 of 6748 papers

TitleStatusHype
Daily Middle-Term Probabilistic Forecasting of Power Consumption in North-East England—0
Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling—0
Arm order recognition in multi-armed bandit problem with laser chaos time series—0
Instance Explainable Temporal Network For Multivariate Timeseries—0
Machine Learning-Based Unbalance Detection of a Rotating Shaft Using Vibration DataCode1
A Bayesian Approach for Predicting Food and Beverage Sales in Staff Canteens and Restaurants—0
Path Imputation Strategies for Signature Models of Irregular Time SeriesCode0
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mappingCode0
Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks—0
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural NetworksCode1
Fractional trends and cycles in macroeconomic time series—0
Emotion-Inspired Deep Structure (EiDS) for EEG Time Series Forecasting—0
COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining—0
Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor DataCode1
On the suitability of generalized regression neural networks for GNSS position time series prediction for geodetic applications in geodesy and geophysics—0
From learning gait signatures of many individuals to reconstructing gait dynamics of one single individual—0
Detecting and explaining changes in various assets' relationships in financial markets—0
RV-FuseNet: Range View Based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting—0
Neural Ordinary Differential Equation based Recurrent Neural Network Model—0
Neural ODEs for Informative Missingness in Multivariate Time Series—0
Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial NetworkCode1
Early Classification of Time Series. Cost-based Optimization Criterion and Algorithms—0
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models—0
Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets—0
Epidemic parameters for COVID-19 in several regions of India—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