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 4101–4150 of 6748 papers

TitleStatusHype
Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series—0
Model-Size Reduction for Reservoir Computing by Concatenating Internal States Through Time—0
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents—0
Entanglement-Embedded Recurrent Network Architecture: Tensorized Latent State Propagation and Chaos Forecasting—0
Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection—0
Distribution Regression for Sequential Data—0
Hierarchical regularization networks for sparsification based learning on noisy datasets—0
Deep learning of contagion dynamics on complex networks—0
Sparse Dynamic Distribution Decomposition: Efficient Integration of Trajectory and Snapshot Time Series DataCode0
Statistical Estimation of High-Dimensional Vector Autoregressive Models—0
Detecting structural perturbations from time series with deep learningCode0
tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder—0
Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine LearningCode0
EnK: Encoding time-information in convolutionCode0
Hybrid Model for Anomaly Detection on Call Detail Records by Time Series Forecasting—0
Online learning of both state and dynamics using ensemble Kalman filters—0
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series—0
Multivariate Functional Singular Spectrum Analysis Over Different Dimensional Domains—0
Dimensionless Anomaly Detection on Multivariate Streams with Variance Norm and Path SignatureCode0
Time Series Analysis and Forecasting of COVID-19 Cases Using LSTM and ARIMA Models—0
Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge—0
Uncovering the mesoscale structure of the credit default swap market to improve portfolio risk modelling—0
Fast CRDNN: Towards on Site Training of Mobile Construction Machines—0
A New Look to Three-Factor Fama-French Regression Model using Sample Innovations—0
AdaVol: An Adaptive Recursive Volatility Prediction MethodCode0
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
Joint Forecasting and Interpolation of Graph Signals Using Deep Learning—0
Frequency Domain Compact 3D Convolutional Neural Networks—0
New Approaches to Robust Inference on Market (Non-)Efficiency, Volatility Clustering and Nonlinear Dependence—0
Discovering Synchronized Subsets of Sequences: A Large Scale SolutionCode0
Theory and Algorithms for Shapelet-based Multiple-Instance LearningCode0
A machine learning approach for forecasting hierarchical time series—0
Sig-SDEs model for quantitative finance—0
Learning Efficient Representations of Mouse Movements to Predict User AttentionCode0
A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers—0
On Regularizability and its Application to Online Control of Unstable LTI SystemsCode0
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
Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling—0
A Bayesian Approach for Predicting Food and Beverage Sales in Staff Canteens and Restaurants—0
Daily Middle-Term Probabilistic Forecasting of Power Consumption in North-East England—0
Arm order recognition in multi-armed bandit problem with laser chaos time series—0
Instance Explainable Temporal Network For Multivariate Timeseries—0
Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks—0
Path Imputation Strategies for Signature Models of Irregular Time SeriesCode0
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mappingCode0
Fractional trends and cycles in macroeconomic time series—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