The data refer to hourly measurements of CO covering the period from 10-03-2004 to 28-02-2005. The aim of the project, given a univariate time series relating to hourly measurements of Oxide of Carbon (CO), is to predict the values of CO in the period between 2005-03-01 and 31-03-2005.
Initially the TS is analyzed graphically in order to understand its trend and possible patterns.
Later we study the stationarity of the time series through two statistical tests. The first test used is the Augmented Dickey-Fuller test which confirms the hypothesis that the TS is stationary. The KPSS test also confirmed this hypothesis. It follows that the time series is stationary and does not require differentiation.
We explored three different approaches to forecast the time series:
- ARIMA (and SARIMA),
- UCM,
- Machine Learning.
In particolar for the ML approaches we explored different architectures: vanilla LSTM, stacked LSTM, bidirectional LSTM and GRU. We discovered that the last NN was the most efficient.
The performance were compared through the MAPE metric which will be used as a reference to evaluate the performance of the models. The ML model was found to be the best:
| Model | MAPE |
|---|---|
| Arima | 10,68 |
| UCM | 12,19 |
| ML (GRU) | 1,8 |