I have a time-series of diurnal temperature range (DTR), 1961 - 2013 from a single weather station. Visually, the first part of the TS seems to have a downward trend, so I used the package
segmented to verify, and specifying 4 break points the downward trend is confirmed.
A downward trend would be significant for my research, but I want to avoid confirmation bias. Could the trend be an artefact of the strong seasonality of the TS?
changepoints to search for step changes of mean value confirms the segmented regression findings, but after attempting to deseasonalize the series by differencing it with lag = 371 days (the maximum ACF value), the trend is completely different.
What I want to ask is: Is it correct to apply segmented regression (and/or changepoints detection) to the raw time-series, or does it need to be pre-processed somehow first?