Using artificial neural networks for continuously decreasing time series data forecasting: A recent strategy for processing complex signals applications to chemistry

Juan Manuel Gutiérrez, Roberto Muñoz, Manel del Valle*

*Corresponding author for this work

Research output: Chapter in BookChapterResearchpeer-review

Abstract

Data preprocessing is an issue that is often recommended to create more uniform data to facilitate ANN learning, meet transfer function requirements, and avoid computation problems. In ANN typical transfer functions, such as the sigmoid logistic function, or the hyperbolic tangent function, cannot distinguish between two very large values, because both yield identical threshold output values of 1.0. It is then necessary to normalize (preprocess) the inputs and outputs of a network. Usually normalization is carried out using the minimum and maximum values obtained in the in-sample (calibration) data. Such a network will result in absurd output, if the out-of-sample (test) data contain values that are beyond the in-sample data range. This ultimately limits the application of ANN in forecasting continuously increasing or decreasing time series data. This study will present a novel and successful application of ANN, which is trained by the error backpropagation algorithm, in the context of forecasting beyond in-sample data range. The emphasis here is on continuously decreasing hydraulic pressure data forecasting that are observed at Mizunami underground research laboratory construction site, Japan. The ANN utilizes the sigmoid logistic function in its hidden and output layers.
Original languageEnglish
Title of host publicationFocus on Artificial Neural Networks
EditorsJohn A. Flores
Pages323-340
Number of pages18
ISBN (Electronic)9781619421004
Publication statusPublished - 12 Feb 2021

Publication series

NameFocus on Artificial Neural Networks

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