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 2476–2500 of 6748 papers

TitleStatusHype
Exact Indexing of Time Series under Dynamic Time Warping—0
Exact Mean Computation in Dynamic Time Warping Spaces—0
Statistical Inference for the Dynamic Time Warping Distance, with Application to Abnormal Time-Series Detection—0
Exact Tests for Offline Changepoint Detection in Multichannel Binary and Count Data with Application to Networks—0
Cine-MRI detection of abdominal adhesions with spatio-temporal deep learning—0
Examining Deep Learning Architectures for Crime Classification and Prediction—0
Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting—0
Excess risk bound for deep learning under weak dependence—0
Exercise Motion Classification from Large-Scale Wearable Sensor Data Using Convolutional Neural Networks—0
Feature-Set-Engineering for Detecting Freezing of Gait in Parkinson's Disease using Deep Recurrent Neural Networks—0
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting—0
Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version—0
Expectation Propagation in Gaussian Process Dynamical Systems—0
Features of the Earth's seasonal hydroclimate: Characterizations and comparisons across the Koppen-Geiger climates and across continents—0
Experimental design trade-offs for gene regulatory network inference: an in silico study of the yeast Saccharomyces cerevisiae cell cycle—0
Ensemble neuroevolution based approach for multivariate time series anomaly detection—0
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction—0
Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal Data Fusion and Feature Extraction—0
Expert Aggregation for Financial Forecasting—0
Causal Structural Learning from Time Series: A Convex Optimization Approach—0
Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations—0
Explainable AI for Time Series via Virtual Inspection Layers—0
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey—0
Ensemble Grammar Induction For Detecting Anomalies in Time Series—0
A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection—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