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 926950 of 6748 papers

TitleStatusHype
Incorporating Stock Market Signals for Twitter Stance DetectionCode0
Inferring Multidimensional Rates of Aging from Cross-Sectional DataCode0
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal DynamicsCode0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Improving COVID-19 Forecasting using eXogenous VariablesCode0
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided EmbeddingCode0
An Image Processing approach to identify solar plages observed at 393.37 nm by the Kodaikanal Solar ObservatoryCode0
A Better Alternative to Piecewise Linear Time Series SegmentationCode0
Improving Accuracy and Explainability of Online Handwriting RecognitionCode0
Gradient Importance Learning for Incomplete ObservationsCode0
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution RobustnessCode0
Identifying Unique Causal Network from Nonstationary Time SeriesCode0
If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GANCode0
Imbedding Deep Neural NetworksCode0
Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image ObservationsCode0
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the PastCode0
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time SeriesCode0
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
Hybrid Deep Neural Networks to Infer State Models of Black-Box SystemsCode0
Human Activity Recognition using Multi-Head CNN followed by LSTMCode0
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processesCode0
Impact of non-stationarity on estimating and modeling empirical copulas of daily stock returnsCode0
Identifying cross country skiing techniques using power meters in ski polesCode0
AverageTime: Enhance Long-Term Time Series Forecasting with Simple AveragingCode0
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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