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 1401–1425 of 6748 papers

TitleStatusHype
Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 MethodsCode0
Classification of Time-Series Images Using Deep Convolutional Neural NetworksCode0
Classification of Time-Series Data Using Boosted Decision TreesCode0
Exploring Interpretable LSTM Neural Networks over Multi-Variable DataCode0
Adaptive-Halting Policy Network for Early ClassificationCode0
Classification of simulated radio signals using Wide Residual Networks for use in the search for extra-terrestrial intelligenceCode0
Explainable time series tweaking via irreversible and reversible temporal transformationsCode0
A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant informationCode0
Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Forecasting with Application to Electricity DemandCode0
Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series PredictionCode0
Explaining Deep Classification of Time-Series Data with Learned PrototypesCode0
A Model of the Fed's View on InflationCode0
ISLAND: Interpolating Land Surface Temperature using land coverCode0
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time SeriesCode0
Classification of multivariate weakly-labelled time-series with attentionCode0
Experimental Study on Time Series Analysis of Lower Limb Rehabilitation Exercise Data Driven by Novel Model Architecture and Large ModelsCode0
Exoplanet Detection using Machine LearningCode0
Kernel Change-point Detection with Auxiliary Deep Generative ModelsCode0
Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of IntelligenceCode0
Conditional Time Series Forecasting with Convolutional Neural NetworksCode0
Machine learning with neural networksCode0
Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series DataCode0
Experimental study of time series forecasting methods for groundwater level predictionCode0
Conditioning non-linear and infinite-dimensional diffusion processesCode0
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flowCode0
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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