Soft Computing in Data Science

Communications in Computer and Information Science(2018)

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This study proposes hybrid methods by combining Singular Spectrum Analysis and Neural Network (SSA-NN) to forecast the currency circulation in the community, i.e. inflow and outflow. The SSA technique is applied to decompose and reconstruct the time series factors which including trend, cyclic, and seasonal into several additive components, i.e. trend, oscillation and noise. This method will be combined with Neural Network as nonlinear forecasting method due to inflow and outflow data have non-linear pattern. This study also focuses on the effect of Eid ul-Fitr as calendar variation factor which allegedly affect inflow and outflow. Thus, the proposed hybrid SSA-NN is evaluated for forecasting time series that consist of trend, seasonal, and calendar variation patterns, by using two schemes of forecasting process, i.e. aggregate and individual forecasting. Two types of data are used in this study, i.e. simulation and real data about the monthly inflow and outflow of 12 currency denominations. The forecast accuracy of the proposed method is compared to ARIMAX model. The results of the simulation study showed that the hybrid SSA-NN with aggregate forecasting yielded more accurate forecast than individual forecasting. Moreover, the results at real data showed that the hybrid SSA-NN yielded as good as ARIMAX model for forecasting of 12 inflow and outflow denominations. It indicated that the hybrid SSA-NN could not successfully handle calendar variation pattern in all series. In general, these results in line with M3 competition conclusion, i.e. more complex methods do not always yield better forecast than the simpler one.
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