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 13761400 of 6748 papers

TitleStatusHype
Algorithms for Learning Graphs in Financial MarketsCode0
A Review of Network Inference Techniques for Neural Activation Time SeriesCode0
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processesCode0
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time SeriesCode0
Extracting Relationships by Multi-Domain MatchingCode0
Data-driven approach in a compartmental epidemic model to assess undocumented infectionsCode0
Explaining Deep Classification of Time-Series Data with Learned PrototypesCode0
Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series PredictionCode0
Complex Gated Recurrent Neural NetworksCode0
A Data Cube of Big Satellite Image Time-Series for Agriculture MonitoringCode0
Explainable time series tweaking via irreversible and reversible temporal transformationsCode0
Exploring Interpretable LSTM Neural Networks over Multi-Variable DataCode0
Fast ES-RNN: A GPU Implementation of the ES-RNN AlgorithmCode0
Classification of Time-Series Images Using Deep Convolutional Neural NetworksCode0
Classification of Time-Series Data Using Boosted Decision TreesCode0
Multimodal Transformer for Unaligned Multimodal Language SequencesCode0
Experimental study of time series forecasting methods for groundwater level predictionCode0
Inferring Density-Dependent Population Dynamics Mechanisms through Rate Disambiguation for Logistic Birth-Death ProcessesCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Adaptive-Halting Policy Network for Early ClassificationCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Classification of simulated radio signals using Wide Residual Networks for use in the search for extra-terrestrial intelligenceCode0
Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shiftCode0
Exoplanet Detection using Machine LearningCode0
Experimental Study on Time Series Analysis of Lower Limb Rehabilitation Exercise Data Driven by Novel Model Architecture and Large ModelsCode0
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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