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 3751–3775 of 6748 papers

TitleStatusHype
Spatiotemporal Attention for Multivariate Time Series Prediction and InterpretationCode0
Airflow recovery from thoracic and abdominal movements using Synchrosqueezing Transform and Locally Stationary Gaussian Process Regression—0
Community recovery in non-binary and temporal stochastic block modelsCode0
Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone—0
A Deep Learning Approach for COVID-19 Trend Prediction—0
Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach—0
Analysis of Thai Capital Market Linkages: Part I. Bivariate Copula ApproachCode0
Application of the Non-Hermitian Singular Spectrum Analysis to the exponential retrieval problem—0
Deep Learning Based on Generative Adversarial and Convolutional Neural Networks for Financial Time Series Predictions—0
k-means on Positive Definite Matrices, and an Application to Clustering in Radar Image SequencesCode0
Error Autocorrelation Objective Function for Improved System Modeling—0
Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and ApplicationCode1
COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning—0
Modeling of time series using random forests: theoretical developments—0
From the logistic-sigmoid to nlogistic-sigmoid: modelling the COVID-19 pandemic growthCode0
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation—0
Forecasting Photovoltaic Power Production using a Deep Learning Sequence to Sequence Model with Attention—0
Remote atrial fibrillation burden estimation using deep recurrent neural network—0
A Causal-based Framework for Multimodal Multivariate Time Series Validation Enhanced by Unsupervised Deep Learning as an Enabler for Industry 4.0—0
Central object segmentation by deep learning for fruits and other roundish objectsCode0
Ubicomp Digital 2020 -- Handwriting classification using a convolutional recurrent network—0
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving ValidationCode0
Principles and Algorithms for Forecasting Groups of Time Series: Locality and GlobalityCode1
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—0
The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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