Predictive Performance Evaluation of ARIMA and Hybrid ARIMA-LSTM Models for Particulate Matter Concentration
This study provides an objective evaluation of prediction performance models for particulate matter policy for industrial stakeholders by comparing the ARIMA and Hybrid ARIMA-LSTM models for predicting air quality data from the industrial environment. In the case of PM 1.0 concentration, we have an...
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Department of Informatics, UIN Sunan Gunung Djati Bandung
2024-12-01
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| author | Johanes Dian Kurniawan Hanna Arini Parhusip Suryasatria Trihandaru |
| author_facet | Johanes Dian Kurniawan Hanna Arini Parhusip Suryasatria Trihandaru |
| author_sort | Johanes Dian Kurniawan |
| collection | DOAJ |
| description | This study provides an objective evaluation of prediction performance models for particulate matter policy for industrial stakeholders by comparing the ARIMA and Hybrid ARIMA-LSTM models for predicting air quality data from the industrial environment. In the case of PM 1.0 concentration, we have an RMSE value of 8.29 and an error ratio of 0.45 for the ARIMA model and an RMSE value of 3.54 and an error ratio of 0.22 for the hybrid ARIMA-LSTM model. Meanwhile, for PM 2.5 concentration, we obtain an RMSE value of 6.61, an error ratio of 0.66 for the ARIMA model, an RMSE value of 2.68, and an error ratio of 0.19 for the hybrid ARIMA-LSTM model. According to this study, the ARIMA model, which is found in autoarima and represents the best model, is (2,0,1) for PM1.0 and (1,0,1) for PM2.5. The hybrid ARIMA-LSTM model outperforms the ARIMA model in terms of prediction accuracy, as evidenced by the RMSE and error ratio values, which are improved by approximately 57.30% and 51.11% for PM1.0 and 59.46% and 71.21% for PM2.5, respectively, since the hybrid ARIMA-LSTM model can accommodate variable-length sequences and capture long-term relationships to become noise-resistant, which makes higher prediction accuracy possible. |
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| institution | Kabale University |
| issn | 2528-1682 2527-9165 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Department of Informatics, UIN Sunan Gunung Djati Bandung |
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| spelling | doaj-art-ff1a17f818974936bca652e90398ff012025-08-20T03:25:55ZengDepartment of Informatics, UIN Sunan Gunung Djati BandungJOIN: Jurnal Online Informatika2528-16822527-91652024-12-019225926810.15575/join.v9i2.13181319Predictive Performance Evaluation of ARIMA and Hybrid ARIMA-LSTM Models for Particulate Matter ConcentrationJohanes Dian Kurniawan0Hanna Arini Parhusip1https://orcid.org/0000-0002-0129-830XSuryasatria Trihandaru2https://orcid.org/0000-0002-7147-1673Master of Data Science, Faculty of Science and Mathematics, Satya Wacana Christian University, SalatigaMaster of Data Science, Faculty of Science and Mathematics, Satya Wacana Christian University, SalatigaMaster of Data Science, Faculty of Science and Mathematics, Satya Wacana Christian University, SalatigaThis study provides an objective evaluation of prediction performance models for particulate matter policy for industrial stakeholders by comparing the ARIMA and Hybrid ARIMA-LSTM models for predicting air quality data from the industrial environment. In the case of PM 1.0 concentration, we have an RMSE value of 8.29 and an error ratio of 0.45 for the ARIMA model and an RMSE value of 3.54 and an error ratio of 0.22 for the hybrid ARIMA-LSTM model. Meanwhile, for PM 2.5 concentration, we obtain an RMSE value of 6.61, an error ratio of 0.66 for the ARIMA model, an RMSE value of 2.68, and an error ratio of 0.19 for the hybrid ARIMA-LSTM model. According to this study, the ARIMA model, which is found in autoarima and represents the best model, is (2,0,1) for PM1.0 and (1,0,1) for PM2.5. The hybrid ARIMA-LSTM model outperforms the ARIMA model in terms of prediction accuracy, as evidenced by the RMSE and error ratio values, which are improved by approximately 57.30% and 51.11% for PM1.0 and 59.46% and 71.21% for PM2.5, respectively, since the hybrid ARIMA-LSTM model can accommodate variable-length sequences and capture long-term relationships to become noise-resistant, which makes higher prediction accuracy possible.https://join.if.uinsgd.ac.id/index.php/join/article/view/1318arimahybrid arima-lstmparticulate matterprediction |
| spellingShingle | Johanes Dian Kurniawan Hanna Arini Parhusip Suryasatria Trihandaru Predictive Performance Evaluation of ARIMA and Hybrid ARIMA-LSTM Models for Particulate Matter Concentration JOIN: Jurnal Online Informatika arima hybrid arima-lstm particulate matter prediction |
| title | Predictive Performance Evaluation of ARIMA and Hybrid ARIMA-LSTM Models for Particulate Matter Concentration |
| title_full | Predictive Performance Evaluation of ARIMA and Hybrid ARIMA-LSTM Models for Particulate Matter Concentration |
| title_fullStr | Predictive Performance Evaluation of ARIMA and Hybrid ARIMA-LSTM Models for Particulate Matter Concentration |
| title_full_unstemmed | Predictive Performance Evaluation of ARIMA and Hybrid ARIMA-LSTM Models for Particulate Matter Concentration |
| title_short | Predictive Performance Evaluation of ARIMA and Hybrid ARIMA-LSTM Models for Particulate Matter Concentration |
| title_sort | predictive performance evaluation of arima and hybrid arima lstm models for particulate matter concentration |
| topic | arima hybrid arima-lstm particulate matter prediction |
| url | https://join.if.uinsgd.ac.id/index.php/join/article/view/1318 |
| work_keys_str_mv | AT johanesdiankurniawan predictiveperformanceevaluationofarimaandhybridarimalstmmodelsforparticulatematterconcentration AT hannaariniparhusip predictiveperformanceevaluationofarimaandhybridarimalstmmodelsforparticulatematterconcentration AT suryasatriatrihandaru predictiveperformanceevaluationofarimaandhybridarimalstmmodelsforparticulatematterconcentration |