Data Engineer, Microsoft
Data Engineer - Capacity Supply Chain and Provisioning, Microsoft India CoE
University of Economics and Technology, Instructor
Head of Data Science, Slated
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.
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Business Overview
A sequence of data points collected often at a constant time interval of a given entity is known as time series. The measure question asked by any business is “how did the past influence the future?”. Forecasting is a process by which the future observation is estimated by using historical data. A statistical method that is used to analyze the data taken over time to forecast the future is known as time series forecasting. It is used to analyze time-based patterns in data and hence to determine a good model to forecast future behavior. Basically time series connect the past, present, and future.
From Supply chain, Stocks to weather, biomedical monitoring forecasting is used everywhere. There are two main use cases in forecasting. The first being store sales prediction to manage inventory demand and plan ahead accordingly. The second is ride-hailing demand for pricing and supply chain management. One of the importance of forecasting is if a holiday comes over how one should plan store sales to get maximum sales hence the profit. In this project, we will use Walmart store sales data to predict store sales. Greykite, a python library developed by Linkedin, and the famous Neural Prophet model developed by Facebook are used to forecast the demand.
Aim
To predict future demand/sales using historical data and other related features.
Data Description
The dataset used is Walmart store sales data. Walmart is an American multinational retail corporation that operates a chain of hypermarkets, department stores, and Grocery stores. The dataset provided is historical sales data for 45 Walmart stores located in different regions. Each store contains many departments. Four different datasets are being provided which are discussed below.
The basic information about the features available in the data is as follows.
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