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 2576–2600 of 6748 papers

TitleStatusHype
Cyclocopula Technique to Study the Relationship Between Two Cyclostationary Time Series with Fractional Brownian Motion Errors—0
Empirical Analysis of Lifelog Data using Optimal Feature Selection based Unsupervised Logistic Regression (OFS-ULR) Model with Spark Streaming—0
A deep network approach to multitemporal cloud detection—0
Cyclical Electromechanical Error Denial System Using Matrix Profile—0
A Tale of Tail Covariances (and Diversified Tails)—0
Curriculum Learning in Deep Neural Networks for Financial Forecasting—0
A tail dependence-based MST and their topological indicators in modelling systemic risk in the European insurance sector—0
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering—0
Current state of nonlinear-type time-frequency analysis and applications to high-frequency biomedical signals—0
Currency exchange prediction using machine learning, genetic algorithms and technical analysis—0
A Systematic Review for Transformer-based Long-term Series Forecasting—0
Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction—0
cs-net: structural approach to time-series forecasting for high-dimensional feature space data with limited observations—0
A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification—0
Analysis of Empirical Mode Decomposition-based Load and Renewable Time Series Forecasting—0
A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services—0
A Comparative Study: Adaptive Fuzzy Inference Systems for Energy Prediction in Urban Buildings—0
A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion from Heartbeat—0
Cellular Traffic Prediction and Classification: a comparative evaluation of LSTM and ARIMA—0
Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk—0
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data—0
Cryptocurrency Market Consolidation in 2020--2021—0
Correlation recurrent units: A novel neural architecture for improving the predictive performance of time-series data—0
Analysis of EEG data using complex geometric structurization—0
Crowdfunding Dynamics Tracking: A Reinforcement Learning Approach—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