Model Identification and Derivation for Double Seasonal Integrated Moving Average (DSARIMA) Model
- DOI
- 10.2991/978-94-6463-014-5_14How to use a DOI?
- Keywords
- DSARIMA; identification; additive; multiplicative; subset
- Abstract
Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) model is an extension of the single SARIMA that is incorporated in modelling data with two seasonality. Model identification, parameter estimation and diagnostic checking are the steps in the modelling. However, the model identification is the most crucial stage as it provides the information used in the next step. Thus, this study extended the derivation of the model identification for DSARIMA in all three models which are additive, multiplicative and subset. The daily and weekly seasonality which can be indicated by 24 and 128 were used in this study with the derivation involving correlation and covariance from the general form of both seasonal and non-seasonal parts. The derivation results were shown for ARIMA (0, 0, 1) (0, 0, 1)24(0, 0, 1)168, ARIMA (0, 0, [1, 24, 25, 168, 169, 192, 193]) and ARIMA (0, 0, [1, 24, 168]) for multiplicative, subset and additive models, respectively. In conclusion, this study gives a valuable insight into the model identification step in DSARIMA models.
- 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.
Cite this article
TY - CONF AU - Puteri Aiman Syahirah Rosman AU - Nur Haizum Abd Rahman PY - 2022 DA - 2022/12/12 TI - Model Identification and Derivation for Double Seasonal Integrated Moving Average (DSARIMA) Model BT - Proceedings of the International Conference on Mathematical Sciences and Statistics 2022 (ICMSS 2022) PB - Atlantis Press SP - 141 EP - 149 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-014-5_14 DO - 10.2991/978-94-6463-014-5_14 ID - Rosman2022 ER -