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 3326–3350 of 6748 papers

TitleStatusHype
Adapting ELM to Time Series Classification: A Novel Diversified Top-k Shapelets Extraction Method—0
In Situ 3D Spatiotemporal Measurement of Soluble Biomarkers in Organoid Culture—0
In-situ animal behavior classification using knowledge distillation and fixed-point quantization—0
Causal Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemic—0
Inspection of methods of empirical mode decomposition—0
EigenNetworks—0
Instance Explainable Temporal Network For Multivariate Timeseries—0
A novel convolutional neural network model to remove muscle artifacts from EEG—0
Joint Forecasting and Interpolation of Graph Signals Using Deep Learning—0
NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education—0
Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware—0
Integrated Fault Diagnosis and Control Design for DER Inverters using Machine Learning Methods—0
Integrated information and dimensionality in continuous attractor dynamics—0
Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks—0
Joint modeling of multiple time series via the beta process with application to motion capture segmentation—0
Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling—0
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions—0
A Novel CNN-LSTM-based Approach to Predict Urban Expansion—0
Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction—0
Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis—0
Day Level Forecasting for Coronavirus Disease (COVID-19) Spread: Analysis, Modeling and Recommendations—0
A Graph-constrained Changepoint Detection Approach for ECG Segmentation—0
DBT-DMAE: An Effective Multivariate Time Series Pre-Train Model under Missing Data—0
Efficient Variational Bayes Learning of Graphical Models with Smooth Structural Changes—0
Efficient Time Series Processing for Transformers and State-Space Models through Token Merging—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