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 3151–3200 of 6748 papers

TitleStatusHype
Can Predominant Credible Information Suppress Misinformation in Crises? Empirical Studies of Tweets Related to Prevention Measures during COVID-19—0
MultiRocket: Multiple pooling operators and transformations for fast and effective time series classificationCode1
Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting—0
Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting—0
Classification Models for Partially Ordered Sequences—0
Multi-Time-Scale Input Approaches for Hourly-Scale Rainfall-Runoff Modeling based on Recurrent Neural Networks—0
Time Series (re)sampling using Generative Adversarial Networks—0
Dynamic imaging using a deep generative SToRM (Gen-SToRM) model—0
Deep Generative SToRM model for dynamic imaging—0
Gesture Recognition in Robotic Surgery: a Review—0
Adaptive Sequential Design for a Single Time-Series—0
Low Rank Forecasting—0
Reservoir Computing with Magnetic Thin Films—0
Low Dimensional Convolutional Neural Network For Solar Flares GOES Time Series ClassificationCode0
Mining the Mind: Linear Discriminant Analysis of MEG source reconstruction time series supports dynamic changes in deep brain regions during meditation sessions—0
AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting—0
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series ForecastingCode2
Adjusting for Autocorrelated Errors in Neural Networks for Time SeriesCode1
Inference of stochastic time series with missing data—0
Embedding Symbolic Temporal Knowledge into Deep Sequential Models—0
Statistical guided-waves-based SHM via stochastic non-parametric time series models—0
Indian Economy and Nighttime Lights—0
Echo State Network for two-dimensional turbulent moist Rayleigh-Bénard convection—0
Identification of brain states, transitions, and communities using functional MRI—0
A fast algorithm for complex discord searches in time series: HOT SAX Time—0
Short-term prediction of Time Series based on bounding techniques—0
Dynamic cyber risk estimation with Competitive Quantile AutoregressionCode0
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting—0
Multi-Time Attention Networks for Irregularly Sampled Time SeriesCode1
Multi-view Integration Learning for Irregularly-sampled Clinical Time SeriesCode0
Optimizing Convergence for Iterative Learning of ARIMA for Stationary Time Series—0
Conditional Generative Models for Counterfactual Explanations—0
A Review of Graph Neural Networks and Their Applications in Power SystemsCode1
VConstruct: Filling Gaps in Chl-a Data Using a Variational Autoencoder—0
Spectrum Attention Mechanism for Time Series Classification—0
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery ClassificationCode1
Multi-Task Time Series Forecasting With Shared Attention—0
Unraveling S&P500 stock volatility and networks -- An encoding-and-decoding approach—0
Short-term daily precipitation forecasting with seasonally-integrated autoencoderCode0
An Optimal Reduction of TV-Denoising to Adaptive Online Learning—0
A symbolic information approach to characterize response-related differences in cortical activity during a Go/No-Go task—0
Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters—0
Tensor-Train Networks for Learning Predictive Modeling of Multidimensional Data—0
A Review on Deep Learning in UAV Remote Sensing—0
Bayesian hierarchical stacking: Some models are (somewhere) usefulCode1
Graphical Models for Financial Time Series and Portfolio Selection—0
Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits—0
To VaR, or Not to VaR, That is the Question—0
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction—0
Evidence and Behaviour of Support and Resistance Levels in Financial 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