IJASCSE Volume 2, Special Issue 1, 2013
نویسندگان
چکیده
In this paper an ANN model is developed to forecast the monthly inflation of Bangladesh as a function of its own previous value. The model selects a feed-forward backpropagation ANN with two inputs, one hidden layer with five hidden neurons and one output as the optimum network. The model is tested with actual time series data of inflation in case of Bangladesh and forecast evaluation criteria. The forecast performance of the ANN model is also compared with different plausible traditional ARIMA based model. One the basis of forecasting accuracy and the evaluation statistics it was observed that RMSE of ANN based forecasts is much less than the RMSE of forecasts based on ARIMA models. So it can be said that forecasting of inflation with ANN offers better performance than the other traditional models.
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