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 1351–1375 of 6748 papers

TitleStatusHype
Feature engineering workflow for activity recognition from synchronized inertial measurement unitsCode0
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEsCode0
Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time series dataCode0
GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge AggregationCode0
Fast Online Deconvolution of Calcium Imaging DataCode0
Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal frameworkCode0
Faster Retrieval with a Two-Pass Dynamic-Time-Warping Lower BoundCode0
Fast ES-RNN: A GPU Implementation of the ES-RNN AlgorithmCode0
Accurate Inference for Adaptive Linear ModelsCode0
Feature Selection for Multivariate Time Series via Network PruningCode0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Hidden Parameter Recurrent State Space Models For Changing Dynamics ScenariosCode0
Apply Artificial Neural Network to Solving Manpower Scheduling ProblemCode0
Hierarchical Probabilistic Model for Blind Source Separation via Legendre TransformationCode0
Factor-Driven Two-Regime RegressionCode0
Comparing Temporal Graphs Using Dynamic Time WarpingCode0
Factor-augmented tree ensemblesCode0
Fairness in Forecasting of Observations of Linear Dynamical SystemsCode0
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time SeriesCode0
Extracting Relationships by Multi-Domain MatchingCode0
Class-Specific Explainability for Deep Time Series ClassifiersCode0
Exploring Interpretable LSTM Neural Networks over Multi-Variable DataCode0
False Negative Reduction in Video Instance Segmentation using Uncertainty EstimatesCode0
Algorithms for Learning Graphs in Financial MarketsCode0
Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series PredictionCode0
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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