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A Model Selection Approach for Time Series Forecasting: Incorporating Google Trends Data in Australian Macro Indicators

This study examined whether the behaviour of Internet search users obtained from Google Trends contributes to the forecasting of two Australian macroeconomic indicators: monthly unemployment rate and monthly number of short-term visitors. We assessed the performance of traditional time series linear...

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Autores principales: Karim, Ali Abdul, Pardede, Eric, Mann, Scott
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453655/
https://www.ncbi.nlm.nih.gov/pubmed/37628174
http://dx.doi.org/10.3390/e25081144
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author Karim, Ali Abdul
Pardede, Eric
Mann, Scott
author_facet Karim, Ali Abdul
Pardede, Eric
Mann, Scott
author_sort Karim, Ali Abdul
collection PubMed
description This study examined whether the behaviour of Internet search users obtained from Google Trends contributes to the forecasting of two Australian macroeconomic indicators: monthly unemployment rate and monthly number of short-term visitors. We assessed the performance of traditional time series linear regression (SARIMA) against a widely used machine learning technique (support vector regression) and a deep learning technique (convolutional neural network) in forecasting both indicators across different data settings. Our study focused on the out-of-sample forecasting performance of the SARIMA, SVR, and CNN models and forecasting the two Australian indicators. We adopted a multi-step approach to compare the performance of the models built over different forecasting horizons and assessed the impact of incorporating Google Trends data in the modelling process. Our approach supports a data-driven framework, which reduces the number of features prior to selecting the best-performing model. The experiments showed that incorporating Internet search data in the forecasting models improved the forecasting accuracy and that the results were dependent on the forecasting horizon, as well as the technique. To the best of our knowledge, this study is the first to assess the usefulness of Google search data in the context of these two economic variables. An extensive comparison of the performance of traditional and machine learning techniques on different data settings was conducted to enable the selection of an efficient model, including the forecasting technique, horizon, and modelling features.
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spelling pubmed-104536552023-08-26 A Model Selection Approach for Time Series Forecasting: Incorporating Google Trends Data in Australian Macro Indicators Karim, Ali Abdul Pardede, Eric Mann, Scott Entropy (Basel) Article This study examined whether the behaviour of Internet search users obtained from Google Trends contributes to the forecasting of two Australian macroeconomic indicators: monthly unemployment rate and monthly number of short-term visitors. We assessed the performance of traditional time series linear regression (SARIMA) against a widely used machine learning technique (support vector regression) and a deep learning technique (convolutional neural network) in forecasting both indicators across different data settings. Our study focused on the out-of-sample forecasting performance of the SARIMA, SVR, and CNN models and forecasting the two Australian indicators. We adopted a multi-step approach to compare the performance of the models built over different forecasting horizons and assessed the impact of incorporating Google Trends data in the modelling process. Our approach supports a data-driven framework, which reduces the number of features prior to selecting the best-performing model. The experiments showed that incorporating Internet search data in the forecasting models improved the forecasting accuracy and that the results were dependent on the forecasting horizon, as well as the technique. To the best of our knowledge, this study is the first to assess the usefulness of Google search data in the context of these two economic variables. An extensive comparison of the performance of traditional and machine learning techniques on different data settings was conducted to enable the selection of an efficient model, including the forecasting technique, horizon, and modelling features. MDPI 2023-07-30 /pmc/articles/PMC10453655/ /pubmed/37628174 http://dx.doi.org/10.3390/e25081144 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Karim, Ali Abdul
Pardede, Eric
Mann, Scott
A Model Selection Approach for Time Series Forecasting: Incorporating Google Trends Data in Australian Macro Indicators
title A Model Selection Approach for Time Series Forecasting: Incorporating Google Trends Data in Australian Macro Indicators
title_full A Model Selection Approach for Time Series Forecasting: Incorporating Google Trends Data in Australian Macro Indicators
title_fullStr A Model Selection Approach for Time Series Forecasting: Incorporating Google Trends Data in Australian Macro Indicators
title_full_unstemmed A Model Selection Approach for Time Series Forecasting: Incorporating Google Trends Data in Australian Macro Indicators
title_short A Model Selection Approach for Time Series Forecasting: Incorporating Google Trends Data in Australian Macro Indicators
title_sort model selection approach for time series forecasting: incorporating google trends data in australian macro indicators
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453655/
https://www.ncbi.nlm.nih.gov/pubmed/37628174
http://dx.doi.org/10.3390/e25081144
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