Time Series Analysis: yesterday, today, tomorrow
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911912083587072 |
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| author | Mackarov, Igor |
| author_facet | Mackarov, Igor |
| contents | Forecasts of various processes have always been a sophisticated problem for statistics and data science. Over the past decades the solution procedures were updated by deep learning and kernel methods. According to many specialists, these approaches are much more precise, stable, and suitable compared to the classical statistical linear time series methods. Here we investigate how true this point of view is. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06453 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Time Series Analysis: yesterday, today, tomorrow Mackarov, Igor Computers and Society Forecasts of various processes have always been a sophisticated problem for statistics and data science. Over the past decades the solution procedures were updated by deep learning and kernel methods. According to many specialists, these approaches are much more precise, stable, and suitable compared to the classical statistical linear time series methods. Here we investigate how true this point of view is. |
| title | Time Series Analysis: yesterday, today, tomorrow |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2406.06453 |