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

Showing 14511475 of 6748 papers

TitleStatusHype
Classification of multivariate weakly-labelled time-series with attentionCode0
Learning to Detect Sepsis with a Multitask Gaussian Process RNN ClassifierCode0
Fairness in Forecasting of Observations of Linear Dynamical SystemsCode0
Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of IntelligenceCode0
False Negative Reduction in Video Instance Segmentation using Uncertainty EstimatesCode0
Fast and Accurate Time Series Classification with WEASELCode0
Applicability and interpretation of the deterministic weighted cepstral distanceCode0
Factor-augmented tree ensemblesCode0
Extracting Relationships by Multi-Domain MatchingCode0
Factor-Driven Two-Regime RegressionCode0
Context-Dependent Semantic Parsing over Temporally Structured DataCode0
Fast and Robust Online Inference with Stochastic Gradient Descent via Random ScalingCode0
Classification and Feature Transformation with Fuzzy Cognitive MapsCode0
Explaining Deep Classification of Time-Series Data with Learned PrototypesCode0
Exploring Interpretable LSTM Neural Networks over Multi-Variable DataCode0
Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load ForecastingCode0
Citation Sentiment Changes AnalysisCode0
Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series PredictionCode0
An Operator Theoretic Approach for Analyzing Sequence Neural NetworksCode0
Explainable time series tweaking via irreversible and reversible temporal transformationsCode0
Exoplanet Detection using Machine LearningCode0
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural NetworksCode0
Experimental study of time series forecasting methods for groundwater level predictionCode0
ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG IdentificationCode0
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records DataCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified