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 3851–3875 of 6748 papers

TitleStatusHype
Latent State Inference in a Spatiotemporal Generative Model—0
Transient Classification in low SNR Gravitational Wave data using Deep Learning—0
Gated Res2Net for Multivariate Time Series AnalysisCode0
Forecasting time series with encoder-decoder neural networks—0
Evaluation of Local Explanation Methods for Multivariate Time Series Forecasting—0
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
Explainable boosted linear regression for time series forecasting—0
Recurrent Graph Tensor Networks: A Low-Complexity Framework for Modelling High-Dimensional Multi-Way Sequence—0
Automatic deep learning for trend prediction in time series data—0
Tropical time series, iterated-sums signatures and quasisymmetric functionsCode0
Indoor environment data time-series reconstruction using autoencoder neural networks—0
Efficient Variational Bayes Learning of Graphical Models with Smooth Structural Changes—0
Online nonnegative CP-dictionary learning for Markovian dataCode0
Simplicial persistence of financial markets: filtering, generative processes and portfolio risk—0
An analysis of deep neural networks for predicting trends in time series data—0
Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTWCode0
Time your hedge with Deep Reinforcement Learning—0
Recent scaling properties of Bitcoin price returns—0
Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems—0
Learning Quantities of Interest from Dynamical Systems for Observation-Consistent Inversion—0
Demand Forecasting of Individual Probability Density Functions with Machine Learning—0
Healthcare Cost Prediction: Leveraging Fine-grain Temporal Patterns—0
Learning Hidden Patterns from Patient Multivariate Time Series Data Using Convolutional Neural Networks: A Case Study of Healthcare Cost Prediction—0
Identifying Grey-box Thermal Models with Bayesian Neural Networks—0
Spatio-Temporal Functional Neural Networks—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