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 276–300 of 6748 papers

TitleStatusHype
PromptCast: A New Prompt-based Learning Paradigm for Time Series ForecastingCode1
Dataset: Impact Events for Structural Health Monitoring of a Plastic Thin PlateCode1
Understanding of the properties of neural network approaches for transient light curve approximationsCode1
Scalable Spatiotemporal Graph Neural NetworksCode1
TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective AttackCode1
Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering AlgorithmCode1
W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series ForecastingCode1
Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomographyCode1
MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed LaplacianCode1
Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)Code1
ARMA Cell: A Modular and Effective Approach for Neural Autoregressive ModelingCode1
SoMoFormer: Multi-Person Pose Forecasting with TransformersCode1
Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer ArchitecturesCode1
Global RTK Positioning in Graphical State SpaceCode1
AA-Forecast: Anomaly-Aware Forecast for Extreme EventsCode1
Stop&Hop: Early Classification of Irregular Time SeriesCode1
From Time Series to Networks in R with the ts2net PackageCode1
Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series DataCode1
An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly DetectionCode1
Expressing Multivariate Time Series as Graphs with Time Series Attention TransformerCode1
Efficient data-driven gap filling of satellite image time series using deep neural networks with partial convolutionsCode1
Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood RiskCode1
Feature-Based Time-Series Analysis in R using the theft PackageCode1
TSInterpret: A unified framework for time series interpretabilityCode1
Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic ForecastingCode1
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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