Addressing Challenges in Time Series Forecasting: A Comprehensive Comparison of Machine Learning Techniques
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916662784032768 |
|---|---|
| author | Mortezanejad, Seyedeh Azadeh Fallah Wang, Ruochen |
| author_facet | Mortezanejad, Seyedeh Azadeh Fallah Wang, Ruochen |
| contents | The explosion of Time Series (TS) data, driven by advancements in technology, necessitates sophisticated analytical methods. Modern management systems increasingly rely on analyzing this data, highlighting the importance of effcient processing techniques. State-of-the-art Machine Learning (ML) approaches for TS analysis and forecasting are becoming prevalent. This paper briefly describes and compiles suitable algorithms for TS regression task. We compare these algorithms against each other and the classic ARIMA method using diverse datasets: complete data, data with outliers, and data with missing values. The focus is on forecasting accuracy, particularly for long-term predictions. This research aids in selecting the most appropriate algorithm based on forecasting needs and data characteristics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20148 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Addressing Challenges in Time Series Forecasting: A Comprehensive Comparison of Machine Learning Techniques Mortezanejad, Seyedeh Azadeh Fallah Wang, Ruochen Machine Learning The explosion of Time Series (TS) data, driven by advancements in technology, necessitates sophisticated analytical methods. Modern management systems increasingly rely on analyzing this data, highlighting the importance of effcient processing techniques. State-of-the-art Machine Learning (ML) approaches for TS analysis and forecasting are becoming prevalent. This paper briefly describes and compiles suitable algorithms for TS regression task. We compare these algorithms against each other and the classic ARIMA method using diverse datasets: complete data, data with outliers, and data with missing values. The focus is on forecasting accuracy, particularly for long-term predictions. This research aids in selecting the most appropriate algorithm based on forecasting needs and data characteristics. |
| title | Addressing Challenges in Time Series Forecasting: A Comprehensive Comparison of Machine Learning Techniques |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.20148 |