I have two time series that represent yearly population estimates from different sources, for some species. In the first species ('Forc') the two series are definetly not consistent despite the overall trend is similar, in the second ('Cot') both trend and fluctuations are somewhat consistent.
Data should be autocorrelated since they represent population abundance, but fitting an ARIMA model (with
forecast package) results in some 0 in the AR(p) parameters.
Series: Forc_Abb ARIMA(0,1,0) Series: Forc_ntot_est ARIMA(0,0,0) with zero mean Series: Cot_Abb ARIMA(1,0,0) with zero mean Series: Cot_ntot_est ARIMA(2,0,0) with zero mean
The question is how to assess consistency of the two proxies for each species.
That is, not only a test for difference of the slopes but something that accounts also for the yearly consistency (e.g. in 'Forc' species, slope is similar but definitely the proxies are not consistent).
Some species (not shown here) show an evident negative or positive trend.
I'm not interested in forecasting.
Are any of these ideas suitable?
GLS with autocorrelation at lag 1 with proxy and year as indipendent variables, plus the interaction term? Do I need to add some polynomial term in 'Cot' species in this case to better fit the peak in 2006-2007?
remove the trend by differencing and compare the differenced series (how?)
adding a changepoint analysis? (e.g.
changepointpackage)? Do I need apply some smoothing function (e.g. LOESS, moving average etc.)?