Hi @fatihah
You also needed to move your query to the right section by using the edit pen option, for now I have moved it to the right category for response from the course mentors.
the split time is 370 as the slicing is related to the series (i.e. period and noise level of 5) and not only period.
If you check the generate synthetic data, the forecast is a seasonal trend of 365 steps but some noise is added as the real world data often has some noise.
In numerical analysis, noise is generated by any random process, such as due to measurement errors and environmental disturbances
Your next doubt
One technique you can use is to do a moving average. This sums up a series of time steps and the average will be the prediction for the next time step. For example, the average of the measurements at time steps 1 to 10 will be the forecast for time step 11, then the average for time steps 2 to 11 will be the forecast for time step 12, and so on.
The function below does the moving average for the entire series. It takes a window_size argument to indicate the number of time steps to consider when computing the mean.
So as the time steps here is taken from step 1 to 10 steps, step 11 is average of step 1- step 10, as the argument mentions
window_size (int) - the number of time steps to compute the average for
Also a general Rule of thumb: A commonly used rule of thumb for choosing the window size is to use 10% to 25% of the length of the time series . For example, if the time series has 100 data points, a window size of 10 to 25 data points might be appropriate.
Regards
DP