Statistical analysis on daily data

Question

I have a number of data points that I am trying to extract a meaningful pattern from (or derive an equation that could then be predictive). I am trying to find a correlation (?) between RANK and DAILY SALES for any given ITEM.

So, for any given item, I have (say) two weeks of daily information, each day consists of a pairing of Inventory, and Rank.

ITEM #1
Monday: 20 in stock (rank 30)
Tuesday: 17 in stock (rank 29)
Wednesday: 14 in stock (rank 31)

The presumption is that 3 items were sold each day, and that selling ~3 a day is roughly what it means to have a rank of ~30.

Given information like this across a wide span (20,000 items, over 2 weeks) of inventory/rank/date pairings, I'd like to derive an equation/method of estimating what the daily sales would be for any given rank.

There's one problem:

The data isn't entirely clean, because -occasionally- the inventory fluctuates upward, either because of re-stocking, or because of returns. So for example, you might see something like

MONDAY: 30 in stock.
TUESDAY: 20 in stock.
WEDNESDAY: 50 in stock.
THURSDAY: 40 in stock.
FRIDAY: 41 in stock.

Indicating that, between Tuesday and wednesday, 30 more were replenished, and on thursday, one was returned.

I am planning to use mean and standard deviation on Daily sales for given rank. So if any rank given I can predict the daily sales based on mean and standard deviation values. Is this correct approach? IS there any better approach for this scenario

Answer 1

Sounds like this could be a good read for you, fpp

It provides an introduction to timeseries forecasting. Timeseries forecasting has a lot of nuance so it can trip people up pretty easily. Some of the issues you have already noted (e.g. seasonality). Others pertain to the statistical properties of such series of data. Take a look through this and