In this paper, two models of forecasting are used the Box-Jenkins procedure employing the SARIMA and the Holt-Winters triple exponential smoothing. Published Consumer Price Index Data from Kenya National Bureau of Statistics (KNBS) for the period November 2011 to October 2016 was used. This paper we equate the forecasted values of both the models and we choose the best model based on the least mean Absolute square error (MASE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The three step model building for Box-Jenkins was first employed, followed by the Hold-Winters triple exponential smoothing. The study found the SARIMA Model was a better model than the Holt-winters triple exponential smoothing as per the obtained results using MASE, MAE and MAPE.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 6, Issue 3) |
| DOI | 10.11648/j.ajtas.20170603.15 |
| Page(s) | 161-169 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Inflation, CPI, Holt-Winters, Triple Exponential Smoothing, ARIMA, SARIMA
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APA Style
Caspah Lidiema. (2017). Modelling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing. American Journal of Theoretical and Applied Statistics, 6(3), 161-169. https://doi.org/10.11648/j.ajtas.20170603.15
ACS Style
Caspah Lidiema. Modelling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing. Am. J. Theor. Appl. Stat. 2017, 6(3), 161-169. doi: 10.11648/j.ajtas.20170603.15
AMA Style
Caspah Lidiema. Modelling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing. Am J Theor Appl Stat. 2017;6(3):161-169. doi: 10.11648/j.ajtas.20170603.15
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Download@article{10.11648/j.ajtas.20170603.15,
author = {Caspah Lidiema},
title = {Modelling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {6},
number = {3},
pages = {161-169},
doi = {10.11648/j.ajtas.20170603.15},
url = {https://doi.org/10.11648/j.ajtas.20170603.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20170603.15},
abstract = {In this paper, two models of forecasting are used the Box-Jenkins procedure employing the SARIMA and the Holt-Winters triple exponential smoothing. Published Consumer Price Index Data from Kenya National Bureau of Statistics (KNBS) for the period November 2011 to October 2016 was used. This paper we equate the forecasted values of both the models and we choose the best model based on the least mean Absolute square error (MASE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The three step model building for Box-Jenkins was first employed, followed by the Hold-Winters triple exponential smoothing. The study found the SARIMA Model was a better model than the Holt-winters triple exponential smoothing as per the obtained results using MASE, MAE and MAPE.},
year = {2017}
}
TY - JOUR T1 - Modelling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing AU - Caspah Lidiema Y1 - 2017/05/25 PY - 2017 N1 - https://doi.org/10.11648/j.ajtas.20170603.15 DO - 10.11648/j.ajtas.20170603.15 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 161 EP - 169 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20170603.15 AB - In this paper, two models of forecasting are used the Box-Jenkins procedure employing the SARIMA and the Holt-Winters triple exponential smoothing. Published Consumer Price Index Data from Kenya National Bureau of Statistics (KNBS) for the period November 2011 to October 2016 was used. This paper we equate the forecasted values of both the models and we choose the best model based on the least mean Absolute square error (MASE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The three step model building for Box-Jenkins was first employed, followed by the Hold-Winters triple exponential smoothing. The study found the SARIMA Model was a better model than the Holt-winters triple exponential smoothing as per the obtained results using MASE, MAE and MAPE. VL - 6 IS - 3 ER -
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