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 3976–4000 of 6748 papers

TitleStatusHype
A New Look to Three-Factor Fama-French Regression Model using Sample Innovations—0
Solar UV-B/A radiation is highly effective in inactivating SARS-CoV-2—0
Prediction of short and long-term droughts using artificial neural networks and hydro-meteorological variables—0
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
Interpretable Time-series Classification on Few-shot SamplesCode1
AdaVol: An Adaptive Recursive Volatility Prediction MethodCode0
Joint Forecasting and Interpolation of Graph Signals Using Deep Learning—0
Detection of gravitational-wave signals from binary neutron star mergers using machine learningCode1
New Approaches to Robust Inference on Market (Non-)Efficiency, Volatility Clustering and Nonlinear Dependence—0
Discovering Synchronized Subsets of Sequences: A Large Scale SolutionCode0
Frequency Domain Compact 3D Convolutional Neural Networks—0
A Generalised Signature Method for Multivariate Time Series Feature ExtractionCode1
Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic RepresentationsCode1
A machine learning approach for forecasting hierarchical time series—0
Theory and Algorithms for Shapelet-based Multiple-Instance LearningCode0
Sig-SDEs model for quantitative finance—0
Learning Efficient Representations of Mouse Movements to Predict User AttentionCode0
On Regularizability and its Application to Online Control of Unstable LTI SystemsCode0
A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers—0
Machine Learning Time Series Regressions with an Application to NowcastingCode1
Generalised Interpretable Shapelets for Irregular Time SeriesCode1
A reproduction rate which perfectly fits Covid-19—0
Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing FlowsCode0
TSML (Time Series Machine Learnng)—0
Recovery of surfaces and functions in high dimensions: sampling theory and links to neural networks—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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