Hybrid SARIMA-FFNN model in forecasting cash outflow and inflow

Abstract

The monthly inflow and outflow of money from an area is one of the important concerns in the economic life of a region. This study aims to model and predict the monthly cash inflow and outflow of Kediri, East Java Province, Indonesia using the Hybrid Seasonal Autoregressive Integrated Moving Average – Feedforward Neural Network (SARIMA-FFNN) model. Seasonal time series data from monthly cash inflow and outflow of Kediri are used to test the forecasting accuracy of the proposed hybrid model. First, both variables are modeled using the SARIMA model. Then, non-linearity testing was carried out on the best SARIMA model for each variable and the results showed that only cash inflow was non-linear. Therefore, only cash inflow could be continued with the FFNN model. The best selected model was the FFNN model with the input SARIMA(0,0,0)(1,0,0)12 with five hidden layers. The input of FFNN modeling was based on the best SARIMA model with only the autoregressive order which for non-seasonal and seasonal. The sum of hidden layers was chosen by the smallest values of MAPE and RMSE. Forecasting results with the hybrid SARIMA-FFNN model on data testing followed the actual data pattern.

A Comparative Study of Arima and Sarima Models to Forecast Lockdowns Due to Covid-19

“…The study on the SARIMA model has shown that this model has various uses in forecasting data related to -Weather and Temperature forecasting [33], Tourism [34], fire frequency or accidents forecasting [35], the financial sector [36], and healthcare industry [37]. Talking about the healthcare industry, the current paper deals with the analysis and forecasting of region-wise smart lockdowns.…”

“…For forecasting the daily tourism arrivals at Macau SAR, China, the SARIMA-LSTM Hybrid model (using 2-year tourism data) is developed by the authors in reference [34] which claims to outperform the previously used SARIMA mo del for the same purpose. In addition, the [36] hybrid SARIMA-FFNN model was tested to predict Kediri money inflows and outflows using hidden layers selected by the minima of mean absolute percentage error (MAPE) and root -meansquared error (RMSE). The hybrid SARIMA-FFNN shows excellent predictive performance.…”