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 1051–1075 of 6748 papers

TitleStatusHype
In-situ animal behavior classification using knowledge distillation and fixed-point quantization—0
Assessing Lower Limb Strength using Internet-of-Things Enabled Chair—0
W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series ForecastingCode1
Valuing Players Over Time—0
Reactmine: a statistical search algorithm for inferring chemical reactions from time series data—0
Causal discovery for time series with latent confoundersCode0
Modular Federated Learning—0
Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach—0
Impact analysis of recovery cases due to COVID19 using LSTM deep learning model—0
Pattern-Based Prediction of Population Outbreaks—0
Features Fusion Framework for Multimodal Irregular Time-series Events—0
Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomographyCode1
Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems—0
Learning Differential Operators for Interpretable Time Series Modeling—0
An Interpretable and Efficient Infinite-Order Vector Autoregressive Model for High-Dimensional Time Series—0
In Situ 3D Spatiotemporal Measurement of Soluble Biomarkers in Organoid Culture—0
An Explainer for Temporal Graph Neural Networks—0
Estimation of Correlation Matrices from Limited time series Data using Machine Learning—0
Universal Fourier Attack for Time Series—0
MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed LaplacianCode1
Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting—0
Progressive Fusion for Multimodal Integration—0
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution—0
Recurrent LSTM-based UAV Trajectory Prediction with ADS-B Information—0
Long-term hail risk assessment with deep neural networks—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