Time Series Determinism Recognition by LSTM Model
The problem of time series determinism measurement is investigated. It is shown that a deep learning model can be used as a determinism measure of a time series. Three distinct time series classes were utilised to verify the feasibility of differentiating deterministic time series: deterministic, de...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/12/2000 |
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| Summary: | The problem of time series determinism measurement is investigated. It is shown that a deep learning model can be used as a determinism measure of a time series. Three distinct time series classes were utilised to verify the feasibility of differentiating deterministic time series: deterministic, deterministic with noise, and stochastic. The LSTM model was constructed for each time series, and its features were thoroughly investigated. The findings of this study demonstrate a strong correlation between the root mean square error (RMSE) of the trained models and the determinism of a time series. |
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| ISSN: | 2227-7390 |