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Statistical Models for Forecasting Petroleum Pump Prices in Kenya
Current Issue
Volume 5, 2018
Issue 3 (September)
Pages: 58-62   |   Vol. 5, No. 3, September 2018   |   Follow on         
Paper in PDF Downloads: 41   Since Sep. 29, 2018 Views: 1050   Since Sep. 29, 2018
Authors
[1]
Lawrence Areba Bichanga, Department of Mathematics and Physics, Kabarak University, Nakuru, Kenya.
Abstract
This paper deals with predicting the mean Petroleum Pump Prices in Kenya by adopting time series statistical tools. In this study, data of mean Petroleum Pump Prices is taken for the period September 15, 2017 – October 14, 2017 to July 15, 2018 – August 14, 2018. Different Exponential smoothing tools and ARIMA models were taken into the consideration for the purpose of analysing the data and predicting the figures of future mean Petroleum Pump Prices in Kenya. Among the several models and tools best model were carved out by using the criteria like MAPE (Mean Absolute Percent Error) and RMSE (Root Mean Square Error) and AIC (Akaike Information Criterion). With the help of these criteria, on validation of the forecasts from these models, Double Exponential Smoothing models performed better than the ARIMA model for Mean Diesel and Kerosene pump prices. ARIMA model performed better than Double exponential Smoothing Model for Mean super Pump prices. These models may helpful to forecast the mean Petroleum Pump Prices in Kenya in upcoming years
Keywords
Exponential Smoothing, ARIMA Model, Petroleum Pump Prices
Reference
[1]
Mehta R. K. (2015) Forecasting Models For Predicting Future Arrival Of International Tourists In India. International Journal of Business Economics & Management Research______ ISSN 2249- 8826 ZIJBEMR, Vol. 5 (10), OCTOBER (2015), pp. 15-26.
[2]
Akuno, A. O., Otieno, M. O., Mwangi, C. W. and Bichanga, L. A. (2015) Statistical Models for Forecasting Tourists’ Arrival in Kenya. Open Journal of Statistics, 5, 60-65. http://dx.doi.org/10.4236/ojs.2015.51008
[3]
Satya, P., Ramasubramanian, V. and Menta, S. C. (2007) Statistical Models for Forecasting Milk Production in India. Journal of the Indian Society of Agricultural Statistics, 61, 80-83.
[4]
Gardener, E. S. (1985) Exponential Smoothing—The State of the Art. Journal of Forecasting, 4, 1-28. http://dx.doi.org/10.1002/for.3980040103
[5]
Jani, P. N. (2014) Business Statistics: Theories and Applications. PHI Learning Private Limited, Delhi.
[6]
Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998) Forecasting: Methods and Applications. John Wiley & Sons, New York.
[7]
Padhan, P. C. (2011) Forecasting International Tourists Footfalls in India: An Assortment of Competing Models. International Journal of Business and Management, 6, 190-202. A. O. Akuno et al. 65.
[8]
Box, G. E. P. and Jenkins, G. M. (1970) Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco.
[9]
Pankratz, A. (1983) Forecasting with Univariate Box-Jenkins Models: Concepts and Cases. John Wiley and Sons, New York. http://dx.doi.org/10.1002/9780470316566
[10]
Hanke, J., & Wichern, D. (2009). Business forecasting. London: Pearson Prentice Hall. (pp. 390-392).
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