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 1751–1775 of 6748 papers

TitleStatusHype
ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions—0
Accurate shape and phase averaging of time series through Dynamic Time Warping—0
Composition Properties of Inferential Privacy for Time-Series Data—0
Evaluating the Planning and Operational Resilience of Electrical Distribution Systems with Distributed Energy Resources using Complex Network Theory—0
Demand Forecasting of Individual Probability Density Functions with Machine Learning—0
Deep Generative SToRM model for dynamic imaging—0
Deep Generators on Commodity Markets; application to Deep Hedging—0
Autoencoding Conditional GAN for Portfolio Allocation Diversification—0
A review on outlier/anomaly detection in time series data—0
Composite FORCE learning of chaotic echo state networks for time-series prediction—0
Deep Hedging, Generative Adversarial Networks, and Beyond—0
Autoencoding Time Series for Visualisation—0
A Method for Massively Parallel Analysis of Time Series—0
Deep Hedging under Rough Volatility—0
Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking—0
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection—0
Composable Generative Models—0
Complex-valued Gaussian Process Regression for Time Series Analysis—0
A review on distance based time series classification—0
Interpretable Deep Representation Learning from Temporal Multi-view Data—0
A Data-Driven Approach for Modeling Stochasticity in Oil Market—0
Automated Detection of Left Ventricle in Arterial Input Function Images for Inline Perfusion Mapping using Deep Learning: A study of 15,000 Patients—0
Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems—0
Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters—0
Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity—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