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 1401–1450 of 6748 papers

TitleStatusHype
Introducing Block-Toeplitz Covariance Matrices to Remaster Linear Discriminant Analysis for Event-related Potential Brain-computer InterfacesCode0
Feature engineering workflow for activity recognition from synchronized inertial measurement unitsCode0
Feature Selection for Multivariate Time Series via Network PruningCode0
Fast Online Deconvolution of Calcium Imaging DataCode0
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEsCode0
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time seriesCode0
Fast ES-RNN: A GPU Implementation of the ES-RNN AlgorithmCode0
Faster Retrieval with a Two-Pass Dynamic-Time-Warping Lower BoundCode0
Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time series dataCode0
Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 MethodsCode0
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented PopulationsCode0
A Model of the Fed's View on InflationCode0
Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series DataCode0
Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health RecordsCode0
Classification of Time-Series Images Using Deep Convolutional Neural NetworksCode0
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving ValidationCode0
Classification of Time-Series Data Using Boosted Decision TreesCode0
Fast and Accurate Time Series Classification with WEASELCode0
Adaptive-Halting Policy Network for Early ClassificationCode0
Conditional Time Series Forecasting with Convolutional Neural NetworksCode0
Classification of simulated radio signals using Wide Residual Networks for use in the search for extra-terrestrial intelligenceCode0
LASSO-ODE: A framework for mechanistic model identifiability and selection in disease transmission modelingCode0
Fast and Robust Online Inference with Stochastic Gradient Descent via Random ScalingCode0
Conditioning non-linear and infinite-dimensional diffusion processesCode0
Condition Monitoring of Drive Trains by Data Fusion of Acoustic Emission and Vibration SensorsCode0
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classificationCode0
Factor-Driven Two-Regime RegressionCode0
Learnable Dynamic Temporal Pooling for Time Series ClassificationCode0
As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learningCode0
Learnable Path in Neural Controlled Differential EquationsCode0
Factor-augmented tree ensemblesCode0
Fairness in Forecasting of Observations of Linear Dynamical SystemsCode0
False Negative Reduction in Video Instance Segmentation using Uncertainty EstimatesCode0
Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series ClassifiersCode0
Classification of multivariate weakly-labelled time-series with attentionCode0
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time SeriesCode0
Exploring Interpretable LSTM Neural Networks over Multi-Variable DataCode0
Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of IntelligenceCode0
Applicability and interpretation of the deterministic weighted cepstral distanceCode0
Explaining Deep Classification of Time-Series Data with Learned PrototypesCode0
Explainable time series tweaking via irreversible and reversible temporal transformationsCode0
Identifying Latent Stochastic Differential EquationsCode0
Extracting Relationships by Multi-Domain MatchingCode0
Few-shot human motion prediction for heterogeneous sensorsCode0
Classification and Feature Transformation with Fuzzy Cognitive MapsCode0
Constrained Generation of Semantically Valid Graphs via Regularizing Variational AutoencodersCode0
Experimental Study on Time Series Analysis of Lower Limb Rehabilitation Exercise Data Driven by Novel Model Architecture and Large ModelsCode0
Learning Physical Concepts in Cyber-Physical Systems: A Case StudyCode0
Learning Procedural Abstractions and Evaluating Discrete Latent Temporal StructureCode0
Exoplanet Detection using Machine LearningCode0
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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