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

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Showing 3826–3850 of 6748 papers

TitleStatusHype
TimePool: Visually Answer "Which and When" Questions On Univariate Time Series—0
TimeREISE: Time-series Randomized Evolving Input Sample Explanation—0
Time Scale Network: A Shallow Neural Network For Time Series Data—0
Time scales in stock markets—0
Time-Series Adaptive Estimation of Vaccination Uptake Using Web Search Queries—0
Time Series Analysis and Forecasting of COVID-19 Cases Using LSTM and ARIMA Models—0
Time Series Analysis and Forecasting of the US Housing Starts using Econometric and Machine Learning Model—0
Time Series Analysis by State Space Learning—0
Time Series Analysis in American Stock Market Recovering in Post COVID-19 Pandemic Period—0
Time Series Analysis in Compressor-Based Machines: A Survey—0
Time Series Analysis of Big Data for Electricity Price and Demand to Find Cyber-Attacks part 2: Decomposition Analysis—0
Time Series Analysis of Electricity Price and Demand to Find Cyber-attacks using Stationary Analysis—0
Time Series Analysis of Key Societal Events as Reflected in Complex Social Media Data Streams—0
Time Series Analysis of Rankings: A GARCH-Type Approach—0
Time Series Analysis of Urban Liveability—0
Time-Series Analysis on Edge-AI Hardware for Healthcare Monitoring—0
Time-Series Analysis via Low-Rank Matrix Factorization Applied to Infant-Sleep Data—0
Time Series Analysis via Network Science: Concepts and Algorithms—0
Time series analysis with dynamic law exploration—0
Time series and machine learning to forecast the water quality from satellite data—0
Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data—0
Time Series Anomaly Detection for Smart Grids: A Survey—0
Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach—0
Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning—0
Time Series Anomaly Detection with label-free Model Selection—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