Intermittent Demand Forecasting Using LSTM With Single and Multiple
Aggregation

Intermittent Demand Forecasting Using LSTM With Single and Multiple
Aggregation

Intermittent Demand, LSTM, ADIDA, MAPA

Intermittent demand data is data with infrequent demand with varying number of demand sizes. Intermittent demand
forecasting is useful for providing inventory control decisions. It is very important to produce accurate forecasts. Based on
previous research, deep learning models, especially MLP and RNN-based architectures, have not been able to provide better
intermittent data forecasting results compared to traditional methods. This research will focus on analyzing the results of
intermittent data forecasting using deep learning with several levels of aggregation and a combination of several levels of
aggregation. In this research, the LSTM model is implemented into two traditional models that use aggregation techniques and
are specifically used for intermittent data forecasting, namely ADIDA and MAPA. The result, based on tests on the six
predetermined data, the LSTM model with aggregation and disaggregation is able to provide better test results than the LSTM
model without aggregation and disaggregation.

Fityan Azizi1
, Wahyu Catur Wibowo2

Universitas Indonesia

31-10-2022

Fajar bagus W

PDF

Indonesia

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