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 951975 of 6748 papers

TitleStatusHype
Impact of non-stationarity on estimating and modeling empirical copulas of daily stock returnsCode0
If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GANCode0
Identifying Unique Causal Network from Nonstationary Time SeriesCode0
Implementing spectral methods for hidden Markov models with real-valued emissionsCode0
Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image ObservationsCode0
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time SeriesCode0
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the PastCode0
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
Identifying cross country skiing techniques using power meters in ski polesCode0
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processesCode0
Nickell Bias in Panel Local Projection: Financial Crises Are Worse Than You ThinkCode0
backShift: Learning causal cyclic graphs from unknown shift interventionsCode0
"Back to the future" projections for COVID-19 surgesCode0
An Image Processing approach to identify solar plages observed at 393.37 nm by the Kodaikanal Solar ObservatoryCode0
Inferring network connectivity from event timing patternsCode0
Gradient Importance Learning for Incomplete ObservationsCode0
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal DynamicsCode0
Human Activity Recognition using Multi-Head CNN followed by LSTMCode0
In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time seriesCode0
AverageTime: Enhance Long-Term Time Series Forecasting with Simple AveragingCode0
A Variational Time Series Feature Extractor for Action PredictionCode0
Hybrid Deep Neural Networks to Infer State Models of Black-Box SystemsCode0
Generalizing to unseen domains via distribution matchingCode0
Auxiliary Quantile Forecasting with Linear NetworksCode0
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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