Optimal adaptive neuro-fuzzy inference system architecture for time series forecasting with calendar effect
This paper discusses a procedure for model selection in ANFIS for time series forecasting with a calendar effect. Calendar effect is different from the usual trend and seasonal effects. Therefore, when it occurs, it will affect economic activity during that period and create new patterns that will r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/19174/ http://journalarticle.ukm.my/19174/1/23.pdf |
| _version_ | 1848814768906829824 |
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| author | Putriaji Hendikawati, Subanar, Abdurakhman, Tarno, |
| author_facet | Putriaji Hendikawati, Subanar, Abdurakhman, Tarno, |
| author_sort | Putriaji Hendikawati, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | This paper discusses a procedure for model selection in ANFIS for time series forecasting with a calendar effect. Calendar effect is different from the usual trend and seasonal effects. Therefore, when it occurs, it will affect economic activity during that period and create new patterns that will result in inaccurate forecasts for decision making if not considered. The focus is on the model selection strategy to find the appropriate input variable and the number of membership functions (MFs) based on the Lagrange Multiplier (LM) test. The ARIMAX stochastic model is used at the preprocessing stage to capture calendar variations in the data. The calendar effect observed is the Eid al-Fitr holiday in Indonesia, a country with the largest Muslim population in the world. The data of Tanjung Priok port passengers used as a case study. The result shows that hybrid ARIMAX-ANFIS based on the LM test can be an effective procedure for model selection in ANFIS for time series with calendar effect forecasting. Empirical results show that the use of the calendar effect variable provides more accurate predictions as indicated by smaller RMSE and MAPE values than without the calendar effect variable. |
| first_indexed | 2025-11-15T00:39:20Z |
| format | Article |
| id | oai:generic.eprints.org:19174 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:39:20Z |
| publishDate | 2022 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:191742022-08-01T04:30:54Z http://journalarticle.ukm.my/19174/ Optimal adaptive neuro-fuzzy inference system architecture for time series forecasting with calendar effect Putriaji Hendikawati, Subanar, Abdurakhman, Tarno, This paper discusses a procedure for model selection in ANFIS for time series forecasting with a calendar effect. Calendar effect is different from the usual trend and seasonal effects. Therefore, when it occurs, it will affect economic activity during that period and create new patterns that will result in inaccurate forecasts for decision making if not considered. The focus is on the model selection strategy to find the appropriate input variable and the number of membership functions (MFs) based on the Lagrange Multiplier (LM) test. The ARIMAX stochastic model is used at the preprocessing stage to capture calendar variations in the data. The calendar effect observed is the Eid al-Fitr holiday in Indonesia, a country with the largest Muslim population in the world. The data of Tanjung Priok port passengers used as a case study. The result shows that hybrid ARIMAX-ANFIS based on the LM test can be an effective procedure for model selection in ANFIS for time series with calendar effect forecasting. Empirical results show that the use of the calendar effect variable provides more accurate predictions as indicated by smaller RMSE and MAPE values than without the calendar effect variable. Penerbit Universiti Kebangsaan Malaysia 2022-03 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/19174/1/23.pdf Putriaji Hendikawati, and Subanar, and Abdurakhman, and Tarno, (2022) Optimal adaptive neuro-fuzzy inference system architecture for time series forecasting with calendar effect. Sains Malaysiana, 51 (3). pp. 895-909. ISSN 0126-6039 https://www.ukm.my/jsm/malay_journals/jilid51bil3_2022/KandunganJilid51Bil3_2022.html |
| spellingShingle | Putriaji Hendikawati, Subanar, Abdurakhman, Tarno, Optimal adaptive neuro-fuzzy inference system architecture for time series forecasting with calendar effect |
| title | Optimal adaptive neuro-fuzzy inference system architecture for time series forecasting with calendar effect |
| title_full | Optimal adaptive neuro-fuzzy inference system architecture for time series forecasting with calendar effect |
| title_fullStr | Optimal adaptive neuro-fuzzy inference system architecture for time series forecasting with calendar effect |
| title_full_unstemmed | Optimal adaptive neuro-fuzzy inference system architecture for time series forecasting with calendar effect |
| title_short | Optimal adaptive neuro-fuzzy inference system architecture for time series forecasting with calendar effect |
| title_sort | optimal adaptive neuro-fuzzy inference system architecture for time series forecasting with calendar effect |
| url | http://journalarticle.ukm.my/19174/ http://journalarticle.ukm.my/19174/ http://journalarticle.ukm.my/19174/1/23.pdf |