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Comparison Of Fuzzy Time Series And ARIMA Model
[Full Text]
AUTHOR(S)
K. Senthamarai Kannan, M. SulaigaBeevi, S. Syed Ali Fathima
KEYWORDS
ACF, ARIMA, , Fuzzy time series, MAE, MSE, PACF, petrol price
ABSTRACT
Crude Oil price, deregulated commodity, which plays a vital criterion in the global economy. Government of India give permission to Oil Companies to revise the price of fuel daily based on the change of international crude oil price and Dollar currency exchange rate. Forecasting is the one of the essential tool to predict the future environment of the fuel price. This paper collates the applications of two Forecasting models such as Auto Regressive Moving Average (ARIMA) model and Fuzzy time series model, on petrol price prediction. The error values, Root Mean Square Error (RMSE), Mean Square Error (MSE) and Mean Absolute Error (MAE) are calculated numerically and graphically for the forecasted values.
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