I'd like to predict visitors in next one or two hours using the previous data.
My question is
- what do you think of my approach below?
- what is the basic and promising approach for this problem?
(I know Box-Jenkins approach, but hope to know better approach.)
My approach is:
- Generate 'base function' representing basic trend which is common for all day
- Estimate the parameters of the 'base function' using previous data on that day
The bell curve (normal distribution) can be utilized as 'base function' and its mean and variance are estimated with previous data on the day.
I think the limitation of my idea is
- the bell curve might be too simple model to fit
- difficulty to estimate the parameter of normal distribution (mu and sigma) with the data of the first several hours.
Edit (following @jdcaballerov's suggestion)
In my preliminary analysis. I've found the following tendency in my data:
- The days different is tiny fluctuation and peak hour (not always 12pm) and the number of customers.
- I could not find regularity for weekday vs weekend or Monday Tuesday,....
- I've already plot hourly visitors (and I've shown in my question) and compared weekday and weekend. (I could not find regurarity on days different)