Time Series Forecasting in Retail

By Data Science Salon

Businesses frequently face the challenge of anticipating the future as accurately as possible when making operational, tactical, and strategic decisions. They want to know if their sales and expenses will increase or decrease in the future, if their stocks will last for several months, and so on. The accessibility of time-stamped data, a sequence of data points indexed in time order, is critical for this forecasting. These data points track change over time and typically consist of periodic measurements taken from the same source across a time interval.

Time series forecasting is studying time-series data and statistics and modeling to make forecasts and strategic decisions. In this blog article, we'll look at the different types of time series forecasting, how they operate, how they're used in retail, and how we create reliable forecasts for accurate decision-making.

Components of Time Series Forecasting

The two types of time series forecasting are univariate and multivariate. While univariate analyzes a single data series, panel/multivariate studies multiple linked series to explore a long-term trend and forecast the future.

The four components of time series forecasting are as follows:

  • Secular trend- Continues to grow in the same direction. It's a statistical pattern that's easy to identify and isn't affected by seasonal or cyclical factors.
  • Seasonal variations or seasonality- Occurs more or less regularly over the course of a year, like a holiday season.
  • Cyclical fluctuations- Ups and downs that arise after specific periods. They are caused by the business cycle's changing phases of prosperity, boom, recession, depression, and recovery. 
  • Irregular variations- Random and inconsistent, making them highly unpredictable. They last for very short durations like days, weeks, and at most months.

How Time Series Forecasting Works

Good forecasting can identify the direction in which the data is evolving. It can uncover true trends and patterns in historical data when working with clean, time-stamped data. According to Jeffrey Yau, Head of Data Science, Store Associate Technology at Walmart Labs, "building trustworthy forecasts" necessitates close collaborations between business stakeholders and the data science team.

In his presentation Time Series Forecasting with Prophet Yau explains Prophet, an open-source library for autonomous forecasting that can fit non-linear business trends with yearly, weekly, and daily seasonality forecasts. 

A good example would be the Thanksgiving and Christmas holiday season, which increases the retail demand for homeware, holiday furnishings, cakes, and clothing.



Applications in Retail

Demand forecasting is an integral feature of any flourishing retail organization. It can be practically impossible to have the right amount of stock on hand at any given time without proper demand forecasting mechanisms in place. Too much inventory in the warehouse implies more capital is locked up in inventory, while too little might result in out-of-stocks, forcing customers to seek solutions from competitors.

In retail, demand forecasting uses data and insights to anticipate how much of a particular product or service people will wish to buy over a set period. This type of predictive analytics aids retailers in determining how much inventory they should keep on hand at any particular time. Almost every retailer is constantly seeking ways to cut costs. It's one of the easiest ways to maximize revenues.

Forecasting can assist merchants in offering the correct product at the right time and place, keeping adequate inventory levels while preventing stockouts, reducing the risk of obsolete inventory, and improving price and promotion management.


Creating Trustworthy Forecasts

The key to creating a practical time series forecasting model is to eliminate as much noise as possible (trend, season, and autocorrelation), which leaves the only remaining movement unaccounted for in the data as pure randomness.

Time series forecasting is not always a simple task. Models that predict and forecast future time points can be built using a variety of strategies. When predicting market movements, it is critical to developing a reliable and robust forecasting model. Close partnerships between business stakeholders and data science teams as well as transparency for business stakeholders are required for an analyst to successfully create a time series forecasting model that is both valid and genuine.

Are you interested in hearing more about how data and AI are used to face challenges in the retail industry? Join us for DSSVirtual | Applying AI & ML to Retail & eCommerce on August 25th to learn from leading data science experts about state-of-the art applications and best practices. Free tier is available for sponsored talks.

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