I have gathered 25 years worth of monthly timeseries data.

The value of Y (dependent variable) has seasonality of 10 months. I have used polynomial equation to model seasonality cycle. The trend is growing which I am using best fit line to forecast.

Finally,

I am calculating residual:

Residual = Y/(trend x seasonality) for each month

However as I move on in time, the range of residuals increase. For example, residual at month 1 is 100 and for the first year, it remains within 50-300. In year 25 month 1, it is 3560. And it remains within 50-4500 for the year 25. This is a much higher range.

I am new to times series analysis and wanted to know how do I model this increase in values?

Is there a name of this type of timeseries issue?

I am using python. Please suggest any pointers.