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, 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.