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 1176–1200 of 6748 papers

TitleStatusHype
Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel DataCode0
ClaSP -- Parameter-free Time Series SegmentationCode1
A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecastingCode1
Conformal Prediction Bands for Two-Dimensional Functional Time Series—0
Time Series Forecasting Models Copy the Past: How to Mitigate—0
Signature-based models: theory and calibrationCode0
Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones—0
Benchmark time series data sets for PyTorch -- the torchtime packageCode1
Forecasting euro area inflation using a huge panel of survey expectations—0
Extending the Range of Robust PCE Inflation Measures—0
Sparse Bayesian State-Space and Time-Varying Parameter Models—0
dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series ClassificationCode1
LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning—0
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series—0
Domain-invariant Feature Exploration for Domain Generalization—0
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Dynamics and triggers of misinformation on vaccines—0
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression ModelingCode0
An NLP-Assisted Bayesian Time Series Analysis for Prevalence of Twitter Cyberbullying During the COVID-19 Pandemic—0
Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification—0
Time Series Prediction under Distribution Shift using Differentiable ForgettingCode0
Anomaly Detection for Fraud in Cryptocurrency Time Series—0
Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images—0
Respecting Time Series Properties Makes Deep Time Series Forecasting PerfectCode1
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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