3 ms·
A larger problem is that time series modeling is particularly resistant towards black box approaches since a lot of information is encoded in the model itself.
by time_to_smile 4y ago
A larger problem is that time series modeling is particularly resistant towards black box approaches since a lot of information is encoded in the model itself.
Take even a simple moving average model on daily observations. Consider stock ticker data (where there are no weekends) and web traffic data (where there is an observation each day). The stock ticker data should be smoothed with a 5 day window and the web traffic with a 7 to help reduce the impact of weekly effects (which probably shouldn't exist in the stock market anyway).
It's possible in either of these cases you might find a moving average that performs better on some choose metric, say 4 or 8 days. However neither of these alternatives make any sense as a window if we're trying to remove day-of-week effect, and unless you can come up with a justifiable explanation, smoothing over arbitrary windows should be avoided.
If you let a black box optimize even a simple moving average you would be avoiding some very essential introspection into what your model is actually claiming.
Not to mention that we often can do more than just prediction with these intentional model tunings (for example day-of-week effect can be explicitly differenced from the data to measure exactly how much sales should increase on a Saturday)
- throwawaythekey 4y agoDo you know of any good resources that explain why smoothing should be used? My intuition is that in your given example (stock prices) smoothing would probably be doing yourself a disservice as it would hide the optimal hour or day of the week to make purchases/sales. Is it mostly related to the timeframe of your analysis and needing to trade off near term precision for longer term precision?
- time_to_smile 4y agoSmoothing is used to negate seasonal effects. Suppose you run a bar, and your busiest days in order are Friday, Saturday, Thursday, Sunday, Wednesday, Tuesday and Monday. Now you are the owner and you want to look at your foot traffic everyday to monitor the health of your business. However, from the ordering I've presented, this will almost never work trivially. Monday traffic will always be less than Sunday, does this mean every Monday you should be concerned about business? Of course not. However by averaging the last 7 days and looking at that each day, you are canceling out these seasonal effects because every single day of the week is accounted for in your measurement. If the 7 day moving average on Monday is less than Sunday you should be concerned because the average when calculated on Sunday included the pervious Monday. For your example, you use the smoothed data and a history of the original data to come up with an exact explanation of which days are the best. For example if you are a bar owner and you don't know which day is the best, you can take a 7 day moving average and subtract it from each day of actual observations. Then averages those differences grouped by day of week and you get an estimate for the day of week effect (you can also calculate standard deviation as well).