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 3626–3650 of 6748 papers

TitleStatusHype
A Sequential Modelling Approach for Indoor Temperature Prediction and Heating Control in Smart Buildings—0
Subjective Metrics-based Cloud Market Performance Prediction—0
Transient Classification in low SNR Gravitational Wave data using Deep Learning—0
Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning ModelsCode1
"Hey, that's not an ODE": Faster ODE Adjoints via SeminormsCode3
Gated Res2Net for Multivariate Time Series AnalysisCode0
Evaluation of Local Explanation Methods for Multivariate Time Series Forecasting—0
Explainable boosted linear regression for time series forecasting—0
TODS: An Automated Time Series Outlier Detection SystemCode2
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
Recurrent Graph Tensor Networks: A Low-Complexity Framework for Modelling High-Dimensional Multi-Way Sequence—0
Forecasting time series with encoder-decoder neural networks—0
Time-series Imputation and Prediction with Bi-Directional Generative Adversarial NetworksCode1
Neural Rough Differential Equations for Long Time SeriesCode1
Automatic deep learning for trend prediction in time series data—0
Indoor environment data time-series reconstruction using autoencoder neural networks—0
Time series forecasting with Gaussian Processes needs priorsCode1
Tropical time series, iterated-sums signatures and quasisymmetric functionsCode0
Simplicial persistence of financial markets: filtering, generative processes and portfolio risk—0
Time your hedge with Deep Reinforcement Learning—0
Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTWCode0
An analysis of deep neural networks for predicting trends in time series data—0
Efficient Variational Bayes Learning of Graphical Models with Smooth Structural Changes—0
TadGAN: Time Series Anomaly Detection Using Generative Adversarial NetworksCode2
Online nonnegative CP-dictionary learning for Markovian dataCode0
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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