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 4926–4950 of 6748 papers

TitleStatusHype
Detection of small changes in medical and random-dot images comparing self-organizing map performance to human detection—0
A Self-supervised Approach to Hierarchical Forecasting with Applications to Groupwise Synthetic Controls—0
Visualizing High Dimensional Dynamical Processes—0
TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series ClassificationCode0
Traffic Flow Combination Forecasting Method Based on Improved LSTM and ARIMA—0
Gesture Recognition in RGB Videos UsingHuman Body Keypoints and Dynamic Time WarpingCode0
Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent UnitsCode0
Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network—0
Metaheuristics optimized feedforward neural networks for efficient stock price prediction—0
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processesCode0
Learning from weakly dependent data under Dobrushin's condition—0
Multifractal cross-correlations between the World Oil and other Financial Markets in 2012-2017—0
Automatic estimation of heading date of paddy rice using deep learning—0
Bayesian Learning from Sequential Data using Gaussian Processes with Signature CovariancesCode0
Normalizing flows for novelty detection in industrial time series data—0
Real-Time Privacy-Preserving Data Release for Smart Meters—0
Anomaly Detection with HMM Gauge Likelihood Analysis—0
Real-time Prediction of Bitcoin Bubble Crashes—0
Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time WarpingCode0
Recurrent Neural ProcessesCode0
A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation—0
Time-warping invariants of multidimensional time seriesCode0
Time scales in stock markets—0
Warping Resilient Scalable Anomaly Detection in Time Series—0
Efficient structure learning with automatic sparsity selection for causal graph processes—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