Forecast of Agricultural Crop Price using Time Series and Kalman Filter Method

This study is an exploratory analysis of the possibility of improving the accuracy and precision of typical time series model in forecasting future prices of rice crop by combining the techniques of ARIMA and Kalman filter respectively. Using actual rice data collected over a period of five years, the performance of the typical ARIMA model was compared to the combined performance of ARIMA-Kalman filter using Mean Square Error(MSE) and Root Mean Square Error(RMSE) as the bases of comparison. Results of the analysis revealed that a more accurate and precise time series estimates of future price of rice can be achieved when the technique of Kalman filter is combined with typical ARIMA time series model. Further analysis showed that predicted values generated by the combined techniques from outof -sample forecasts are fairly closer to the actual values. On the basis of the findings of this study, the development of a time series software that will work on combining the algorithms of the ARIMA and Kalman filter is recommended. Keyword: ARIMA, Kalman Filter, Forecast, Time Series . INTRODUCTION Agricultural price information is crucial to enable farmers and traders to make better decisions about how to grow, when to harvest, to which markets produce should be sent, and to whether store agricultural crop or not. Predictions of agricultural crop prices involve a large number of calculations based on different forecasting models. Time series prediction refers to the process by which the future values of a system are forecasted based on the information obtained from the past and the current point data points. Time-series methods use time as independent variables to produce demand. In a time series, measurements are taken at successive points or over successive periods. The measurements may be taken every hour, day, week, month, or year, or at any other regular (or irregular) interval. Time series models are characterized by four components: trend component, cyclical component, seasonal component, and irregular component[1]. On one hand, Kalman filter is a device that separates a time series in two components, one called signal and the other is noise. Signals are contaminated by ‘noise’ from a variety of sources and it is only when estimated of true values are available that the next location may be estimated with any accuracy. The Kalman Filter is a very good tool and has been applied in a wide variety of estimation applications. The basic idea behind the filter is simple to arrive at a conditional density function of the unobservable, the functional form of relationship with observable equation of motion and assumptions regarding the distribution of error terms. The filter uses the current observation to predict the next period’s value of the unobservable and then uses the realization next period to update that forecast. Recently, combinational tools and approaches have attracted researches in the field of forecasting. McIntosh and Bessler [2] conducted a study on forecasting agricultural prices using Bayesian approach utilizing matrix beta priors. The study explains the matrix beta approach and applies it to three individual forecasts namely ARIMA and ROLS approach using regression analysis and adaptive weighting technique. The results indicate that, given these data and a quadratic loss performance metric (MSFE), the analyst would have been better off than a composite forecast rather than attempting to identify a best individual forecast. Lately, Jha & Sinha [3] and Shahwan & Odening [4] has attracted their attention to Artificial Neural Network (ANN) as an alternative technique for estimation and forecasting. The superiority of ANN over ARIMA linear model methodology has been applied using monthly wholesale price series of agricultural products. The experimental analysis has Cenas, Forecast of Agricultural Crop Price using Time Series and Kalman Filter Method __________________________________________________________________________________________________________________ 16 P-ISSN 2350-7756 | E-ISSN 2350-8442 | www.apjmr. com Asia Pacific Journal of Multidisciplinary Research, Vol. 5, No. 4, November 2017 Part III shown that ANN models are able to attain a significant number of directions of monthly price change as compared to the ARIMA linear models. Further, it has also been observed that combining linear and nonlinear models lead to more accurate forecasts than the performances of these models independently. A study by Moghaddam and Saeed Ebrahimijam [5] on a predictive model using extended Kalman filter which simultaneously fuses information and parameters of technical and fundamental analysis to predict short term stock prices. Then, as a real test, the model implemented for the shares of one of the industrial companies in Iran. Finally, the obtained results will be compared with other method results such as regression and neural networks which show its desirability in short-term predictions. Therefore, due to its wide applicability and relevance, the importance of a thorough study on the performance of time series statistical techniques of fixed and open models is confirmed, specifically in the agricultural prices analysis context. Clements and Hendry [6] argue that a combination of forecasts may be superior to each of individual forecasts if it meets the following: the forecasting models do not draw from a common information pool, are of a different nature and type, or when models are differently sensitive to structural breaks and misspecification. Choosing the right forecasting method to be used highly depends on the structure of time series, and as stated by Umar [7] to simplicity, accuracy and stability of the data. Choosing the best forecasting method for any particular situation is not a simple task Armstrong and Green [8] and sometimes more than one method may be appropriate based on accuracy measures such as MSE, MAD and other methods. This research attempts to formulate a new agricultural crop price forecasting model that has accurate predicting capability. OBJECTIVES OF THE STUDY This research attempted to forecast future prices of agricultural crop using time series and Kalman filter combined, more specifically tried to attain the following specific objectives: to characterize the data needed to produce accurate forecasts the prices of agricultural crops and to determine if there is an existing data to extract; and to improve the prediction accuracy of agricultural crop prices using a combination model of Time Series and Kalman filter prediction algorithms. MATERIALS AND METHODS Research Design This research utilized exploratory research to determine the method of building a model of an accurate tool that will forecast the future agricultural crop prices. This study explored the possibility of combining two forecasting tools to generate a model that accurately forecasted the future agricultural crop prices using actual historical data. Data This study used purposive sampling to gather needed information because of specific purpose and the nature of the needed data in building a forecasting model. The agricultural crops price series was analyzed based on a series of monthly prices. These prices were nominal prices and were compiled from the National Statistics Office (NSO) and Philippine Agricultural Statistic Office. Two periods were used to analyze the forecasting models, an in-sample and an out-of-sample period. The series from the in-sample period was used to generate the forecasting models, the out-of-sample period was utilized to check against the accuracy of the model based on the out –of –sample data. According to Dhuyvetter et al. [9] and Ye et al. [10] the main criterion for choosing this period is that its corresponding closeness to the period of desired time of forecasting. The latest data closely available to the desired period were from 1990 to 2014. Therefore, the in-sample period were series of agricultural crop prices from January 2007 to December 2011 and an out-of-sample period which was composed of series prices from January 2012 to December 2014. Conceptual Framework The relationship between previous agricultural crop prices and the process that will forecast the future prices of agricultural crops can be conceptualized at a fairly general level as depicted in the conceptual framework. Phase 1. ARIMA Modeling Stage Stage 1. Identification On the identification stage, identify specific response agricultural crop price series and possible candidate data for ARIMA models to use. Stationarity tests can be performed to determine if differencing is necessary. In this analysis, there will be several outputs of ARIMA models that could be fit. Asia Pacific Journal of Multidisciplinary Research, Vol. 5, No. 4, November 2017 __________________________________________________________________________________________________________________ 17 P-ISSN 2350-7756 | E-ISSN 2350-8442 | www.apjmr. com Asia Pacific Journal of Multidisciplinary Research, Vol. 5, No. 4, November 2017 Part III Input Process Output