Intermittent Demand Forecasting Using LSTM With Single and Multiple
Aggregation
Dublin Core
Title
Intermittent Demand Forecasting Using LSTM With Single and Multiple
Aggregation
Aggregation
Subject
Intermittent Demand, LSTM, ADIDA, MAPA
Description
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.
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.
Creator
Fityan Azizi1
, Wahyu Catur Wibowo2
, Wahyu Catur Wibowo2
Publisher
Universitas Indonesia
Date
31-10-2022
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Fityan Azizi1
, Wahyu Catur Wibowo2, “Intermittent Demand Forecasting Using LSTM With Single and Multiple
Aggregation,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9267.
Aggregation,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9267.