Szczegóły publikacji
Opis bibliograficzny
Machine learning methods for time series prediction of the diffusion curve / Mateusz ZARĘBA, Marta Skiba // Archives of Mining Sciences = Archiwum Górnictwa ; ISSN 0860-7001 . — 2025 — vol. 70 no. 4, s. 561-574. — Bibliogr. s. 573-574, Abstr. — Publikacja dostępna online od: 2025-12-17
Autorzy (2)
- AGHZaręba Mateusz
- Skiba Marta
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165105 |
|---|---|
| Data dodania do BaDAP | 2026-01-08 |
| Tekst źródłowy | URL |
| DOI | 10.24425/ams.2025.157447 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Archives of Mining Sciences = Archiwum Górnictwa |
Abstract
This study employed machine learning techniques to predict time series of diffusion curves, generated in Python with NumPy library. The data was structured as a time series to enable efficient model training and evaluation. Various approaches – statistical, neural, and regression-based – were tested to model the diffusion dynamics. Results revealed notable performance differences: regression models achieved the highest accuracy with the lowest error rates, while time series models like ARIMA and TCN performed worse, likely due to difficulties in capturing the process’s complexity.
