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 4326–4350 of 6748 papers

TitleStatusHype
Equivalence relations and L^p distances between time series with application to the Black Summer Australian bushfires—0
Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data—0
Uncovering differential equations from data with hidden variables—0
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction—0
Sharpe Ratio Analysis in High Dimensions: Residual-Based Nodewise Regression in Factor Models—0
Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in Massive IoT Networks—0
Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network—0
Error-feedback stochastic modeling strategy for time series forecasting with convolutional neural networks—0
Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective—0
DYNOTEARS: Structure Learning from Time-Series DataCode1
Variable-lag Granger Causality and Transfer Entropy for Time Series AnalysisCode1
Model Extraction Attacks against Recurrent Neural Networks—0
Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation—0
Two-Sample Testing for Event Impacts in Time SeriesCode0
Automated Deep Abstractions for Stochastic Chemical Reaction NetworksCode0
Semantic Discord: Finding Unusual Local Patterns for Time SeriesCode0
Towards a Kernel based Uncertainty Decomposition Framework for Data and Models—0
Signatures of brain criticality unveiled by maximum entropy analysis across cortical states—0
A Time-Series Distribution Test System Based on Real Utility Data—0
Ensemble Grammar Induction For Detecting Anomalies in Time Series—0
Bayesian Neural Architecture Search using A Training-Free Performance MetricCode1
Analysis, Online Estimation, and Validation of a Competing Virus Model—0
TDEFSI: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information—0
Dynamic clustering of time series data—0
WISDoM: characterizing neurological timeseries with the Wishart distribution—0
Show:102550
← PrevPage 174 of 270Next →
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