SSA does a good job of extracting trend and seasonality from a time series. However widening the window on a large dataset puts quite a load on my CPU (GPU is too old to contribute to calculation in Julia) heats up my CPU quite noticeably. A window of 20 on a data series of 20000 goes quite nicely and finishes without stressing the CPU, but 200 and particularly 2000 window puts a strain on my old i5-4460. So I use
sensors and kill the process if it gets too hot.
Flux has a useful callback feature in the
train! function which permits building in a check for a hot CPU which is very useful to ensure that you keep the state intact. Ensuring that data is in Float32 format also helps.
I recently renewed the paste on the processor and reseated the fan so I know that is good, and I don’t want to limit the assigned cores; what other measures possible to keep from melting down?