Time Series Analysis for Statistical Forecasting
Analyzing the past data and getting insights on future demand
Maybe you could wonder how you forecast your business for the next months. A possible solution could be: “I study all my SKU histories, then I try to forecast my business”. But…
- is it a scalable strategy?
- what if the number of SKUs is huge?
- what about the forecast quality?
- are there automatic tools to do this with less effort and more quality?
Time Series Analysis is more than a simple solution. Basically, every product in your catalog have a past sales history, which is simply a sequence of consecutive numbers related to, for example, past months sales. Every sequence is called time series.
Time series analysis is a set of automatic and mathematical tools suitable for every kind of business able to extract all the relevant features from time series (cyclic, seasonal and trend patterns), in order to do the statistical forecasting.
Ok but…how can those tools do that?
First of all, they treat all the time series as unidimensional signals trying to understand if there are, for example, correlations between consecutive months or between a month and the same month but in the previous year.
They are able to detect trend components, analyzing the derivative of the signal, in a way such that, they can detect not only its presence, but also its influence on the entire signal.
What about seasonality patterns? Nobody better than you knows your business, so you know that you have seasonal products or not. Time Series Analysis tools give you something more, answering the question: “How much last summers could influence this one? How many years have to be considered?”
When a set of relevant characteristics is detected, a mathematical model is built over the considered time series. This model is representative of the time series and it can be used to do the statistical forecasting of the product for a prefixed number of periods.