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 2151–2175 of 6748 papers

TitleStatusHype
The leverage effect and other stylized facts displayed by Bitcoin returns—0
A fast algorithm for complex discord searches in time series: HOT SAX Time—0
A Consistent Method for Learning OOMs from Asymptotically Stationary Time Series Data Containing Missing Values—0
Independence Testing for Temporal Data—0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—0
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders—0
Denoising diffusion probabilistic models for probabilistic energy forecasting—0
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning—0
Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework—0
Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity—0
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree—0
Demand Forecasting of Individual Probability Density Functions with Machine Learning—0
Demand Forecasting in Smart Grid Using Long Short-Term Memory—0
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting—0
An Explainer for Temporal Graph Neural Networks—0
Aedes-AI: Neural Network Models of Mosquito Abundance—0
Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models—0
Demand Forecasting for Electric Vehicle Charging Stations using Multivariate Time-Series Analysis—0
A Winner-Take-All Approach to Emotional Neural Networks with Universal Approximation Property—0
A windowed correlation based feature selection method to improve time series prediction of dengue fever cases—0
Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia—0
Defined the predictors of the lightning over India by using artificial neural network—0
An Experimental Evaluation of Nearest Neighbour Time Series Classification—0
A dynamic conditional approach to portfolio weights forecasting—0
A consistent deterministic regression tree for non-parametric prediction of 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