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 1301–1325 of 6748 papers

TitleStatusHype
Learning PDE Solution Operator for Continuous Modeling of Time-Series—0
A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics—0
Sparse High-Dimensional Vector Autoregressive Bootstrap—0
Inference in Non-stationary High-Dimensional VARs—0
Deep learning for ψ-weakly dependent processes—0
Time-warped Trials—0
Adaptive hedging horizon and hedging performance estimation—0
Monitoring the risk of a tailings dam collapse through spectral analysis of satellite InSAR time-series data—0
Time Series Forecasting via Semi-Asymmetric Convolutional Architecture with Global Atrous Sliding Window—0
Graph Anomaly Detection in Time Series: A Survey—0
A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction—0
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver BehaviourCode0
Online estimation methods for irregular autoregressive models—0
Benchmarking optimality of time series classification methods in distinguishing diffusionsCode0
Data-driven soiling detection in PV modules—0
Wavelet Analysis for Time Series Financial Signals via Element Analysis—0
Approximating DTW with a convolutional neural network on EEG data—0
BSSAD: Towards A Novel Bayesian State-Space Approach for Anomaly Detection in Multivariate Time Series—0
Global Flood Prediction: a Multimodal Machine Learning Approach—0
Time-Series Pattern Recognition in Smart Manufacturing Systems: A Literature Review and Ontology—0
Multidimensional dynamic factor models—0
Localizing the Origin of Idiopathic Ventricular Arrhythmia from ECG Using an Attention-Based Recurrent Convolutional Neural Network—0
Optimizing a Bayesian method for estimating the Hurst exponent in behavioral sciences—0
GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network—0
Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based PerspectiveCode0
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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