New member and rather new to statistics / times series problems. I am an electro - mechanical engineer by trade and am tasked with solving with what I believe to be a times series problem. I am looking for some insight as to how I might approach said problem :

I have a DAQ ( data acquisition computer system ) that monitors low voltage signals from a giant water pump motor system. The DAQ provides a new signal every 15 minutes through out the day ( hence the times series question ). I am analyzing these low voltage signals with the ( ICWT ... inverse continuous wavelet transform in Matlab). With each new update, the series changes. I expect this as I'm adding an element to a series. Specifically I am wondering how to proceed in normalizing ( or stabilizing ) the time series so that the changes in the analysis in the underlying ICWT are not so drastic. In other words, maintain the approx trend of the signal. As you can see in the attached screenshot, this particular signal fluctuates between . 6898 and .6918. Currently, I am re-analyzing the entire time series each time a new low voltage element is added to the series. This causes radical shifts in the ICWT analysis. The objective here is to 'catch electrical anomalies before they cause significant damage'. I use the words 'normalize' and 'stabilize' because I am unsure how to describe analyzing or transforming the time series to achieve this effect.

I have read a bit about stationarity, differencing, auto correlation, ARIMA, etc but am unsure if any of those processes would be appropriate here. Would appreciate any suggestions. Hope this makes sense.

Thank You,

Richard