Time Series Forecasting Models: A Comparative Study of some Models with Application to Inflation Data
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Osabuohien-Irabor Osarumwense , Department of Mathematics/Statistics, Ambrose Alli University, Ekpoma, Nigeria.
This study examined and compared six basic time series forecasting models (Exponential model, Double Exponential model, Holt-Winter models, Time Series linear regression model, the ad-hoc Bootstrapping model and the Self Adjusting model) with application to twenty-four Months Nigeria’s CPI inflation sample data, from January 2009 to December 2010 inflation data. With the aids of five different standard forecasting accuracy measures (MSE, MAE, RMSE, SSE, and MAPE), results from the out-of-sample forecasts shows that the double exponential model with a smoothening constant of 0.68 is the best forecasting model for the Nigeria inflation rate data among the other ad-hoc model considered.
Forecast, Error, Exponential, Smoothening, Constant, Inflation
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