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 4551–4575 of 6748 papers

TitleStatusHype
Learning Disentangled Representations of Satellite Image Time Series—0
Learning dynamic Boltzmann machines with spike-timing dependent plasticity—0
Learning Dynamics and Structure of Complex Systems Using Graph Neural Networks—0
Learning Ensembles of Anomaly Detectors on Synthetic Data—0
Learning Financial Networks with High-frequency Trade Data—0
Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting—0
Learning from multivariate discrete sequential data using a restricted Boltzmann machine model—0
Learning from weakly dependent data under Dobrushin's condition—0
Learning Generalized Causal Structure in Time-series—0
Learning Hamiltonian dynamics by reservoir computer—0
Learning Heteroscedastic Models by Convex Programming under Group Sparsity—0
Learning Hidden Markov Models Using Conditional Samples—0
Learning Hidden Patterns from Patient Multivariate Time Series Data Using Convolutional Neural Networks: A Case Study of Healthcare Cost Prediction—0
Learning Individualized Cardiovascular Responses from Large-scale Wearable Sensors Data—0
Learning in Feedforward Neural Networks Accelerated by Transfer Entropy—0
Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting—0
Learning Latent Causal Dynamics—0
Learning latent causal relationships in multiple time series—0
Learning Leading Indicators for Time Series Predictions—0
Learning Linear Dynamical Systems with High-Order Tensor Data for Skeleton based Action Recognition—0
Learning Mixtures of Linear Dynamical Systems—0
Learning Mixture Structure on Multi-Source Time Series for Probabilistic Forecasting—0
Learning Network of Multivariate Hawkes Processes: A Time Series Approach—0
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process—0
Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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