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20101
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Subject: Agricultural Statistics × Author: Anil Kumar.H.S. × End date: 2017 × Start date: 2010 ×
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    ThesisItemOpen Access
    Evaluation of statistical models in price forecasting- A case of timber trade
    (UAS, Dharwad, 2010) Anil Kumar.H.S.; R.B.Naik
    Timber price was considered as the important variable affecting the optimality of forest management. On the other hand, forecasting of timber price is very uncertain. The present study was conducted to study the behavior of different forecasting models in timber price forecasting from Dandeli and Kiruvatti depot. The information on price was collected from the respective depots for the study period from 1980 to 2009. In the present investigation different forecasting models like trend analysis, ANN model, Box-Jenkins model and Exponential smoothing models are considered to produce forecast and to measure the forecast accuracy among selected different models. The pattern of two different timbers (Teak and Indian Kino) prices from two different depots are analyzed for price movement, including the existence of trend and seasonality component and also the Stationarity property. Price forecasts based on various time series forecasting methods are produced and compared with the published forecasts. The forecast errors obtained from different models are following normal distribution from both timber depots, there by satisfying one of the assumptions of forecasting models. In case of teak price forecasting Box-Jenkins (ARIMA) model was best validated for both the timber depots with the higher correlation coefficient and the lesser coefficient of variation. In case of Indian Kino price forecasting, Box-Jenkins (ARIMA) model for Dandeli depot and Single exponential smoothing model for Kiruvatti depot was best validated. Finally Box- Jenkins (ARIMA) model was found to be best forecasting model by producing minimum value of MAPE and RMSE for Dandeli depot. Where as for Kiruvatti depot Box-Jenkins (ARIMA) model was found to be best in Teak price forecasting and Single exponential smoothing model was found best in forecasting Indian Kino price with a minimum value of MAPE and RMSE.