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

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Showing 4876–4900 of 6748 papers

TitleStatusHype
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processesCode0
Multifractal cross-correlations between the World Oil and other Financial Markets in 2012-2017—0
Learning from weakly dependent data under Dobrushin's condition—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
Time scales in stock markets—0
Real-time Prediction of Bitcoin Bubble Crashes—0
Time-warping invariants of multidimensional time seriesCode0
Recurrent Neural ProcessesCode0
Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time WarpingCode0
A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation—0
Warping Resilient Scalable Anomaly Detection in Time Series—0
GluonTS: Probabilistic Time Series Models in PythonCode3
Extending Deep Learning Models for Limit Order Books to Quantile Regression—0
Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications—0
Efficient structure learning with automatic sparsity selection for causal graph processes—0
Probabilistic Forecasting with Temporal Convolutional Neural NetworkCode3
Efficient Kernel-based Subsequence Search for User Identification from Walking Activity—0
Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions—0
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend FilteringCode0
Time-Series Anomaly Detection Service at MicrosoftCode1
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—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