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A GAMM can model both the periodic component (using a cyclical spline) and the temporal correlation structure. Since fitting ARMA beyond the AR1 is very slow, the below code example only serves as illustration, to be modified according to the residuals' partial ACF: x <- read.table(pipe("pbpaste")) # read data from clipboard on macos y <- as.vector(t(...


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R has very good facilities for automatic time series forecasting, which I very much recommend. Here is what R does with your data, specifically an exponential smoothing model in state space form (ETS for "Error, Trend, Seasonality"): library(forecast) churn <- structure(c(0.9854712144, 1.000828964, 1.000828964, 1.000828964, 1.004811044, 1.006802085, 1....


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The question is quite specific, but an answer can be based on general principles. If the data generating process happens to be better approximated* by an integrated VAR model than by a seasonally-integrated VAR model, your may be better of with the former when forecasting. When could this be the case? Whenever your time series are not seasonally integrated ...


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Are you dealing with simple or complex seasonality in your data? For example, monthly data may show monthly seasonality whereas hourly data may show daily and weekly seasonality. I think your decomposition would need to reflect whether the seasonality in your data is simple or complex. Simple seasonality would produce a single seasonal component when ...


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on repeated trials it shows that daily seasonality is pretty much zero (though my algorithms suggest that there is one.) I am not sure about Prophet, but STL will fit a seasonal component whether or not one is present. This may account for your observation. I personally would fit seasonal and non-seasonal models to your data and assess forecast accuracy on ...


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Before attempting to model the seasonal effect, I would use a simple t-test to confirm the existence of a seasonal effect. Divide the observations into subsets and check the subset for significant difference from the total dataset of observations. The null hypothesis is the observations in the subset have the same mean as a random sample from the total ...


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