It’s taken me 3 attempts to understand this.
Time series decomposition and forecasting.
I got a C during time series forecasting during my master’s program. The only C I’ve ever gotten during that course (and it was because of time series). It was somewhat deserved so the review has been needed. I think the students would have been better served if the professor taught it in R or python rather than self writing his own material and relying on a commercial product that expired as soon as the semester was over (Forecast Pro XE). As it was he never covered additive time series decomposition fully. I guess in his defense. Most students were newb’s when it came to coding, so expecting them to deploy it in R would be a bit much when we were simply learning to clean time series and derive p scores (I had one course that really dived into R and I know I have learned 300% since then on proper R usage). However, he did teach the basics of decomposition, smoothing methods, arima, and autocorrelation, as well as advanced models like holt’s & winter’s exponential smoothing methods.
But I finally got it.
It’s still a work in progress, as is I just wanted to understand the decomposition on the in sample before I did a proper holdout analysis.
I now understand why some time series decompositions include a cycle factor and trend rather than just a trend-cycle.
I intend on doing forecasts of
Linear (i.e. with a cycle factor)
Using auto.arima to forecast out either cycle factor or trend-cycle depending on the model above.
It’s still a bit messy, I’ve been using this long weekend to get it brain dumped into the IDE after reviewing multiple notes (3rd times a charm). I use excel to model it before I move it into code (something I mentioned during interview recently and was told that’s how a lot of people do it). I figure it will take 3 coding passes to get the code clean and probably won’t be pristine until I have it properly ported into python.
But v1 will be done this weekend and I’ll have a forecast of Los Angeles Condo prices.