A solution to seasonal adjustment forecasting for hydraulic SMEs in investment decision-making

Seasonal adjustment plays a significant role in the analysis of current social and economic conditions, particularly in determining the stages of the business cycle at which the economy stands. Nowadays the most popular method for seasonal adjustment in time series forecasting is the X-12-ARIMA algorithm, etc. All of these methods are sophisticated and difficult to understand and not suitable for small and medium enterprises (SMEs) to forecast and make decisions, especially for hydraulic SMEs which are troubled about low ROI and natural issues. To stimulate private investment in hydraulic industry the seasonal effects should be averaged out to smooth the investment decision making. Thus we introduce the Centered Moving Average (CMA) method, the modification to the moving average method, which is simple and easy to operate for hydraulic SMEs. By using a case, we illustrate the procedure carefully. Comparing the accuracy of forecasts between the actual data trend line and the deseasonalized data trend line, we can conclude that the accuracy improves greatly when using the CMAs. It is simple and low cost and more suitable for hydraulic SMEs.