Artificial Neural Network with Histogram Data Time Series Forecasting: A Least Squares Approach Based on Wasserstein Distance

DOI10.1007/978-3-030-49728-6_23zbMath1475.62245OpenAlexW3047680239MaRDI QIDQ5015946

Kongliang Zhu, Woraphon Yamaka, Pichayakone Rakpho

Publication date: 10 December 2021

Published in: Studies in Computational Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-49728-6_23

zbMATH Keywords

autoregressive modelartificial neural networkWasserstein distancehistogram data

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Cites Work

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• Time series forecasting using a hybrid ARIMA and neural network model
• Basic statistics for distributional symbolic variables: a new metric-based approach
• Linear regression for numeric symbolic variables: a least squares approach based on Wasserstein distance
• Symbolic Data Analysis
• Linear regression model with histogram‐valued variables
• How the Maximal Evidence of P-Values Against Point Null Hypotheses Depends on Sample Size

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