Forecasting daily sales is crucial for operating a restaurant because it directly impacts decisions about stock levels, helping to reduce food waste and optimize customer satisfaction. Most small to medium-sized restaurants forecast their daily sales based on historical records or simply on experience. However, with the growing trend of AI, many restaurants are now interested in implementing this new technology into their business. This presents another common challenge for them because AI technology includes complex models and mathematical theories.
Therefore, before delving into the world of AI forecasts, it's crucial to understand that various models exist, and grasping their underlying theories can greatly benefit you in making informed decisions. Among these models, the SARIMAX model stands out as a powerful tool specifically tailored for addressing the challenges of sales forecasting. This guide will help you understand how to easily use SARIMAX models to forecast your sales and enhance your inventory management practices.
What is the SARIMAX Model?
The SARIMAX model (Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors) is an extension of the ARIMA model used for time series forecasting. SARIMAX incorporates both seasonality and external variables (exogenous regressors) into the forecasting process, making it particularly useful for complex time series data commonly encountered in real-world applications, such as restaurant sales forecasting.
How to Implement the SARIMAX Model for Restaurant Sales Forecasting?
Implementing the SARIMAX model to forecast your restaurant's sales involves several steps, from data collection and preparation to model fitting and forecasting. Here's a detailed guide to help you through the process:
1. Collect and Prepare Your Data
Data Collection: The initial step involves gathering historical sales data, encompassing daily sales figures and any pertinent categories. Additionally, collect exogenous variables such as holidays, weather conditions, promotions, and events.
Data Cleaning: Upon data compilation, it may not consistently adhere to time-series format requirements. Raw data sets might lack essential values or dates, rendering them unsuitable for model training. Therefore, mastering proper data cleansing and preparation is crucial. Address null values and fill in missing dates with the previous day’s values to ensure the data is primed for analysis and forecasting.
2. Identify the SARIMAX Parameters
The SARIMAX model is typically represented as SARIMAX(p,d,q)×(P,D,Q,s), where:
- p: Number of lag observations included in the model (autoregressive part).
- d: Number of times that the raw observations are different (integrated part).
- q: Size of the moving average window.
- P: Number of seasonal autoregressive terms.
- D: Number of seasonal differences.
- Q: Number of seasonal moving average terms.
- s: The period of the seasonality
When selecting a value for s, it's essential to understand the cyclic nature of your seasonal data. For instance, if your data points are monthly and the seasonal cycle spans a year, set s to 12. Similarly, if your data points are daily and the seasonal cycle is weekly, set s to 7.
The next step is to determine the optimal SARIMAX model parameters. In this phase, we leverage powerful tools like pmdarima to assist us in this endeavor.
3. Evaluate the SARIMAX Model
Apply the identified model parameters to the SARIMAX model and evaluate the model's fit. Use the fitted model to forecast future sales.
4. Implement and Adjust Your Forecasting
Integrate the forecasted values into your inventory management system to adjust stock levels accordingly. Monitor and refine your model as new data becomes available.
By following these steps, you can effectively implement the SARIMAX model for restaurant sales forecasting, helping you make informed decisions and optimize your inventory management practices.
How to Forecast Your Sales Smarter?
For small to medium-sized restaurant owners seeking smarter sales forecasting solutions but unsure about implementing SARIMAX into the current forecasting method, Strinno offers expertise and support. Contact us today to explore how we can assist you in integrating forecasting technology into your inventory management decisions.
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