Hybrid ARIMA-ANN Model for Solving Nonlinearity in Time Series Data

DOI

10.2991/978-94-6463-332-0_9How to use a DOI?

Keywords

ARIMA; shallot prices; Hybrid ARIMA-ANN; MAPE

Abstract

Shallots (Allium cepa L. var. Aggregatum) are one of the most important culinary spices in the world. Shallots contain vitamin C, potassium, dietary fiber, folic acid, calcium and iron. Because the nutritional content and benefits of shallots are very complete, the demand for shallots is quite high in various regions in Indonesia. It takes an evaluation process of shallot price data to determine the future movement of shallot prices through the right forecasting method to obtain accurate shallot price predictions. In this study, the forecasting was conducted using the Hybrid Autoregressive Integrated Moving Average - Artificial Neural Network (ARIMA-ANN) method, because the data has a nonlinear pattern. The results of this forecasting can be used as a reference to predict shallot prices for the next few periods. The analysis steps are data preparation, modeling and forecasting using ARIMA, forecasting residual of ARIMA model using ANN method, and then, forecasting results are obtained from the difference between actual data values and residual forecasting results.The comparison results with ARIMA method show that forecasting using Hybrid ARIMA-ANN method produces a good accuracy value. Data models with ARIMA and Hybrid ARIMA-ANN to predict shallot prices in this study have MAPE 19.81% and 19.1%, respectively. Based on these results, the Hybrid Autoregressive Integrated Moving Average - Artificial Neural Network (ARIMA-ANN) method is very well used to forecast shallot prices.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Widyanti Rahayu
AU  - Vera Maya Santi
AU  - Dania Siregar
AU  - Dimas Kuncoro Djati
PY  - 2023
DA  - 2023/12/18
TI  - Hybrid ARIMA-ANN Model for Solving Nonlinearity in Time Series Data
BT  - Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)
PB  - Atlantis Press
SP  - 70
EP  - 76
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-332-0_9
DO  - 10.2991/978-94-6463-332-0_9
ID  - Rahayu2023
ER  -