Demand-Forecasting

  • Business Problem

A chain of stores, 10 different stores and 50 different requests a 3-month demand forecast for its product.

  • Dataset Story

This dataset is presented to test different time series techniques.

Information of 10 different stores and 50 different products in 5-year data of a chain of stores is located.

  • Variables

date – Date of sales data

~No holiday effects or store closures

Store – Store ID

~Unique number for each store.

Item - Item ID

Unique number for each product.

Sales – Number of products sold,

~The number of products sold from a particular store on a given date

  • Task
  • Relevant store using the following time series and machine learning techniques
  • Create a 3-month demand forecasting model for the chain.

Random Noise

Lag/Shifted Features

Rolling Mean Features

Exponentially Weighted Mean Features

Custom Cost Function (SMAPE)

Model Validation with LightGBM

GitHub - nurrturkaslan/Demand-Forecasting at pythonawesome.com
A chain of stores, 10 different stores and 50 different requests a 3-month demand forecast for its product. - GitHub - nurrturkaslan/Demand-Forecasting at pythonawesome.com