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 1601–1625 of 6748 papers

TitleStatusHype
Currency exchange prediction using machine learning, genetic algorithms and technical analysis—0
Current state of nonlinear-type time-frequency analysis and applications to high-frequency biomedical signals—0
Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems—0
Curriculum Learning in Deep Neural Networks for Financial Forecasting—0
Deep Learning for Energy Time-Series Analysis and Forecasting—0
Cyclical Electromechanical Error Denial System Using Matrix Profile—0
Cyclocopula Technique to Study the Relationship Between Two Cyclostationary Time Series with Fractional Brownian Motion Errors—0
D2KE: From Distance to Kernel and Embedding—0
DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation—0
Daily Middle-Term Probabilistic Forecasting of Power Consumption in North-East England—0
Dalek -- a deep-learning emulator for TARDIS—0
A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers—0
CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting—0
DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity—0
DANLIP: Deep Autoregressive Networks for Locally Interpretable Probabilistic Forecasting—0
DANNTe: a case study of a turbo-machinery sensor virtualization under domain shift—0
DANTE: A framework for mining and monitoring darknet traffic—0
Darts: User-Friendly Modern Machine Learning for Time Series—0
DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing—0
Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform—0
Deep Learning for Time-Series Analysis—0
Data Augmentation for Multivariate Time Series Classification: An Experimental Study—0
Data Augmentation techniques in time series domain: A survey and taxonomy—0
A Time-Frequency based Suspicious Activity Detection for Anti-Money Laundering—0
Deep Metric Learning with Locality Sensitive Angular Loss for Self-Correcting Source Separation of Neural Spiking Signals—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