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 976–1000 of 6748 papers

TitleStatusHype
Inferring Multidimensional Rates of Aging from Cross-Sectional DataCode0
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the PastCode0
AverageTime: Enhance Long-Term Time Series Forecasting with Simple AveragingCode0
Identifying cross country skiing techniques using power meters in ski polesCode0
Investigating Enhancements to Contrastive Predictive Coding for Human Activity RecognitionCode0
Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal DynamicsCode0
I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and EmbeddingCode0
A Variational Time Series Feature Extractor for Action PredictionCode0
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
An Information Theory Approach on Deciding Spectroscopic Follow UpsCode0
Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image ObservationsCode0
Generalizing to unseen domains via distribution matchingCode0
Bayesian Online Changepoint DetectionCode0
Auxiliary Quantile Forecasting with Linear NetworksCode0
Kernel Change-point Detection with Auxiliary Deep Generative ModelsCode0
Hybrid Deep Neural Networks to Infer State Models of Black-Box SystemsCode0
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time SeriesCode0
Homological Time Series Analysis of Sensor Signals from Power PlantsCode0
Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series PredictionCode0
Autoregressive Convolutional Neural Networks for Asynchronous Time SeriesCode0
A bootstrap test to detect prominent Granger-causalities across frequenciesCode0
Highly Scalable and Provably Accurate Classification in Poincare BallsCode0
Human Activity Recognition using Multi-Head CNN followed by LSTMCode0
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processesCode0
Inferring network connectivity from event timing patternsCode0
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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