SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain
- PMID: 32361446
- PMCID: PMC7175856
- DOI: 10.1016/j.scitotenv.2020.138883
SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain
Abstract
This study aimed to predict the short-term of confirmed cases of covid-19 and IBEX in Spain by using SutteARIMA method. Confirmed data of Covid-19 in Spanish was obtained from Worldometer and Spain Stock Market data (IBEX 35) was data obtained from Yahoo Finance. Data started from 12 February 2020-09 April 2020 (the date on Covid-19 was detected in Spain). The data from 12 February 2020-02 April 2020 using to fitting with data from 03 April 2020 - 09 April 2020. Based on the fitting data, we can conducted short-term forecast for 3 future period (10 April 2020 - 12 April 2020 for Covid-19 and 14 April 2020 - 16 April 2020 for IBEX). In this study, the SutteARIMA method will be used. For the evaluation of the forecasting methods, we applied forecasting accuracy measures, mean absolute percentage error (MAPE). Based on the results of ARIMA and SutteARIMA forecasting methods, it can be concluded that the SutteARIMA method is more suitable than ARIMA to calculate the daily forecasts of confirmed cases of Covid-19 and IBEX in Spain. The MAPE value of 0.036 (smaller than 0.03 compared to MAPE value of ARIMA) for confirmed cases of Covid-19 in Spain and was in the amount of 0.026 for IBEX stock. At the end of the analysis, this study used the SutteARIMA method, this study calculated daily forecasts of confirmed cases of Covid-19 in Spain from 10 April 2020 until 12 April 2020 i.e. 158925; 164390; and 169969 and Spain Stock Market from 14 April 2020 until 16 April 2020 i.e. 7000.61; 6930.61; and 6860.62.
Keywords: Covid-19; IBEX; Short-term forecast; SutteARIMA.
Copyright © 2020 Elsevier B.V. All rights reserved.
Conflict of interest statement
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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References
-
- Ahmar A.S. A comparison of α-Sutte Indicator and ARIMA methods in renewable energy forecasting in Indonesia. Int. J. Eng. Technol. 2018;7:20–22.
-
- Ahmar A.S. Working Paper; 2019. Reliability test of SutteARIMA to forecast artificial data. - DOI
-
- Ahmar A.S., Rahman A., Mulbar U. α- Sutte Indicator: a new method for time series forecasting. J. Phys. Conf. Ser. 2018;1040
-
- Anokye R., Acheampong E., Owusu I., Isaac Obeng E. Time series analysis of malaria in Kumasi: using ARIMA models to forecast future incidence. Cogent Soc. Sci. 2018;4 doi: 10.1080/23311886.2018.1461544. - DOI
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