I have an assignment to forecast snow depth at a certain location at a specific date. My approach to forecast this is to basically take previous years snowfall data and compare it to this years data so far. I need to figure out which snowfall pattern over the last 50 years is most correlated with this years pattern so far. I have matlab at my disposal and I have all the time series data but I don't know the best way to measure correlation between a certain years data and the current years data statistically.
I have thought about doing an absolute value difference between this years daily data and a given previous years, then averaging it to find the average difference. Whichever one has the smallest average difference will be the year I choose to base the prediction off of. I don't think this is the most accurate method though.
I am not very knowledgeable in statistics and want to be able to show that I used a statistically accurate method to measure correlation when I turn in my report.
Best Answer
Situations like yours are often treated using Exponential Smoothing. However, this works better if you have monthly data; if your data are daily, you may want to look through some of Rob Hyndman's work for extensions.
I strongly recommend this free online forecasting textbook, also by Hyndman.