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 3551–3575 of 6748 papers

TitleStatusHype
StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures—0
Statistical analysis and stochastic interest rate modelling for valuing the future with implications in climate change mitigation—0
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators.—0
Statistical analysis of Wasserstein GANs with applications to time series forecasting—0
Statistical and Computational Guarantees for the Baum-Welch Algorithm—0
Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers—0
Statistical Estimation of High-Dimensional Vector Autoregressive Models—0
Statistical guided-waves-based SHM via stochastic non-parametric time series models—0
Statistical inference of lead-lag at various timescales between asynchronous time series from p-values of transfer entropy—0
Statistical Inference with Stochastic Gradient Methods under φ-mixing Data—0
Statistical learning method for predicting density-matrix based electron dynamics—0
Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix—0
Statistically Significant Detection of Linguistic Change—0
Statistical Modeling and Forecasting of Automatic Generation Control Signals—0
Statistical modeling of isoform splicing dynamics from RNA-seq time series data—0
Statistical properties and multifractality of Bitcoin—0
Statistical Properties of the Entropy from Ordinal Patterns—0
#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs—0
STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles—0
STEER: Simple Temporal Regularization For Neural ODEs—0
STEER : Simple Temporal Regularization For Neural ODE—0
Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention Network—0
STING: Self-attention based Time-series Imputation Networks using GAN—0
STJLA: A Multi-Context Aware Spatio-Temporal Joint Linear Attention Network for Traffic Forecasting—0
ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data—0
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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