Kirsty Parsons (Peak)
Forecasting is a well-established discipline; however, the complexities of time series data often requires expert knowledge using very specific modelling approaches to generate accurate forecasts. At Peak, we come across a range of forecasting projects in retail and manufacturing industries. A few of the business challenges we have faced when forecasting for businesses are:
Forecasting during holidays and events:
The demand for certain products may spike during holidays such as Christmas or large events such as the SuperBowl and retailers need to be able to forecast accordingly. Events can also encompass promotions and sales where they hope that the distribution of demand will change given an attractive offer.
Hierarchical forecasting of products (including product cannibalisation):
When forecasting at a SKU level there may not be enough data to accurately forecast (specifically at a size level) therefore pooling together data at different levels can help improve forecast accuracy.
Forecasting for new products:
Retailers need to know how many of a new product to have in store. Using metadata new products can be compared to similar products and the historical demand of those products used to forecast.
In this presentation, I will briefly discuss the above challenges and show a case study where we used Facebook’s open-source Prophet forecasting model. Prophet is an easy to use model, allowing users of any experience to generate accurate forecasts. The framework has been built to allow any user to adjust the model parameters using very intuitive methods to provide an optimum forecast. We have found it to be a successful model for its ease of use and in particular for forecasting during events and holidays.
What is the nature of your talk? Practical
Does your talk require prior knowledge of the subject area? Some
Is your talk accessible and relevant to practitioners? Somewhat
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