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Journal : edumatic jurnal pendidikan informatika

Beyond Predictive Accuracy: Enhancing Parameter Stability in Multicollinear Time Series Forecasting via Regularisation Faisa, Daffa Kumara Khiar; Salam, Abu
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33925

Abstract

Multicollinearity in feature-based time series regression arises as a structural consequence of lagged and rolling feature construction. However, existing studies on Ridge and ElasticNet regularization adopt an accuracy-driven evaluation paradigm, with limited attention to parameter stability, shrinkage behavior, and sensitivity to regularization strength. This study shifts the evaluation of regularized linear models from predictive accuracy toward stability-oriented assessment. Using daily electricity consumption data from the UCI Repository, Linear Regression, Ridge, and ElasticNet models are examined under engineered temporal features derived from stability-based lag pruning, rolling statistics, and correlation-informed feature selection. Model evaluation focuses on bias–variance behavior, coefficient shrinkage, regularization sensitivity, and training–testing performance gaps. The results show that regularization improves stability, with the performance gap decreasing from 0.0961 in Linear Regression to 0.0608 under ElasticNet. These comparisons show that regularization stabilizes regression models via distinct shrinkage mechanisms, informing model selection beyond accuracy. Ridge exhibits conservative shrinkage averaging 6.06%, whereas ElasticNet induces stronger shrinkage averaging 46.32% and shows higher sensitivity to penalty strength. These findings provide methodological evidence that regularization in feature-based time series regression should be treated as a stability strategy rather than an accuracy optimization tool, offering guidance for electricity load forecasting under structurally redundant temporal features.
Co-Authors Adhitya Nugraha Alan Ma’ruf, Farda Alpiana, Vika Alzami, Farrikh Anisatawalanita Ukhifahdhina Ardin, Akbar Ilham Ardytha Luthfiarta Arifianti, Fidela Putri Arifin, Muhammad Farhan Astuti, Yani Parti Candra Irawan Catur Supriyanto Catur Supriyanto Damaswara, Silvester Aditya Debrina Luna Arghata Mangkawa Diana Aqmala Dimas Pratama, Yohanes Diyan Adiatma Dzaki, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Erlin Dolphina Erwin Yudi Hidayat Erwin Yudi Hidayat Etika Kartikadarma Fahmi Amiq Faisa, Daffa Kumara Khiar Farda Alan Ma'ruf Ferry Bintang Nugroho Fitriyani, Shelomita Haresta, Alif Agsakli Ifan Rizqa Ika Novita Dewi Iskandar, Deo Andrianto Juli Ratnawati Junta Zeniarja Kahingide, Hastyantoko Dwiki Khafiizh Hastuti Kurniawan, Defri Kurniawan, Wira Adi Kusmiyati Kusmiyati L. Budi Handoko Lesmarna, Salsabila Putri Maulana, Fadhli Faqih Maulia, Aenur Hakim Megantara, Rama Aria Moh. Sholik Muhammad Jamhari Mukti, David Ramantya Muljono Muljono Mulyanto, Edy Muzaki, Rizki Nuzul Nabila, Talitha Safa Nafanda, Cynthia Dwi Norman, Maria Bernadette Chayeenee Octaviani, Dhita Aulia Pamungkas, Tahta Arya Paramita, Cinantya Pawidya, Novandra Putra Prinantyo, Gilang Djati Putra, Aditya Herdiansyah Ramadhan Rakhmat Sani Ramadhan, Irfan Surya Restu Agung Pamuji Ricardus Anggi Pramunendar Riyan Ardiansyah Safira, Almira Zuhrotus Sidiq, Syaiful Rizal Sindhu Rakasiwi Susanto, Mario Given Utomo, Danang Wahyu Verdian Putra Wicaksana Wibowo, Isro' Rizky Winarsih, Nurul Anisa Sri Yonismara, Arvie Arvearie