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 1901–1925 of 6748 papers

TitleStatusHype
DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions—0
Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection—0
Compensatory model for quantile estimation and application to VaR—0
Defined the predictors of the lightning over India by using artificial neural network—0
Accurate Prediction of Global Mean Temperature through Data Transformation Techniques—0
Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia—0
Disk storage management for LHCb based on Data Popularity estimator—0
A review of predictive uncertainty estimation with machine learning—0
Demand Forecasting for Electric Vehicle Charging Stations using Multivariate Time-Series Analysis—0
Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models—0
Demand Forecasting in Smart Grid Using Long Short-Term Memory—0
Demand Forecasting of Individual Probability Density Functions with Machine Learning—0
Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity—0
Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework—0
A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data—0
Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression—0
Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases—0
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders—0
A Review of Open Source Software Tools for Time Series Analysis—0
Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling—0
Comparison of PCA with ICA from data distribution perspective—0
Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective—0
A Review of Mathematical and Computational Methods in Cancer Dynamics—0
Dependent Matérn Processes for Multivariate Time Series—0
Comparison of Machine Learning Methods for Predicting Karst Spring Discharge in North China—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