Forecast Sales using ARIMA and SARIMA

Dataset

This dataset was created by ChakraMLOps

Released under Apache 2.0

Contents

Context and Methodology

Research Domain:
The dataset is part of a project focused on retail sales forecasting. Specifically, it is designed to predict daily sales for Rossmann, a chain of over 3,000 drug stores operating across seven European countries. The project falls under the broader domain of time series analysis and machine learning applications for business optimization. The goal is to apply machine learning techniques to forecast future sales based on historical data, which includes factors like promotions, competition, holidays, and seasonal trends.

Purpose:
The primary purpose of this dataset is to help Rossmann store managers predict daily sales for up to six weeks in advance. By making accurate sales predictions, Rossmann can improve inventory management, staffing decisions, and promotional strategies. This dataset serves as a training set for machine learning models aimed at reducing forecasting errors and supporting decision-making processes across the company’s large network of stores.

How the Dataset Was Created:
The dataset was compiled from several sources, including historical sales data from Rossmann stores, promotional calendars, holiday schedules, and external factors such as competition. The data is split into multiple features, such as the store's location, promotion details, whether the store was open or closed, and weather information. The dataset is publicly available on platforms like Kaggle and was initially created for the Kaggle Rossmann Store Sales competition. The data is made accessible via an API for further analysis and modeling, and it is structured to help machine learning models predict future sales based on various input variables.

Technical Details

Dataset Structure:

The dataset consists of three main files, each with its specific role:

1. Train:
   This file contains the historical sales data, which is used to train machine learning models. It includes daily sales information for each store, as well as various features that could influence the sales (e.g., promotions, holidays, store type, etc.).

   https://handle.test.datacite.org/10.82556/yb6j-jw41
   PID: b1c59499-9c6e-42c2-af8f-840181e809db

2. Test2:
   The test dataset mirrors the structure of train.csv but does not include the actual sales values (i.e., the target variable). This file is used for making predictions using the trained machine learning models. It is used to evaluate the accuracy of predictions when the true sales data is unknown.

   https://handle.test.datacite.org/10.82556/jerg-4b84
   PID: 7cbb845c-21dd-4b60-b990-afa8754a0dd9

3. Store:
   This file provides metadata about each store, including information such as the store’s location, type, and assortment level. This data is essential for understanding the context in which the sales data is gathered.

   https://handle.test.datacite.org/10.82556/nqeg-gy34
   PID: 9627ec46-4ee6-4969-b14a-bda555fe34db

Data Fields Description:

• Id: A unique identifier for each (Store, Date) combination within the test set.

• Store: A unique identifier for each store.

• Sales: The daily turnover (target variable) for each store on a specific day (this is what you are predicting).

• Customers: The number of customers visiting the store on a given day.

• Open: An indicator of whether the store was open (1 = open, 0 = closed).

• StateHoliday: Indicates if the day is a state holiday, with values like:

  • 'a' = public holiday,

  • 'b' = Easter holiday,

  • 'c' = Christmas,

  • '0' = no holiday.

• SchoolHoliday: Indicates whether the store is affected by school closures (1 = yes, 0 = no).

• StoreType: Differentiates between four types of stores: 'a', 'b', 'c', 'd'.

• Assortment: Describes the level of product assortment in the store:

  • 'a' = basic,

  • 'b' = extra,

  • 'c' = extended.

• CompetitionDistance: Distance (in meters) to the nearest competitor store.

• CompetitionOpenSince[Month/Year]: The month and year when the nearest competitor store opened.

• Promo: Indicates whether the store is running a promotion on a particular day (1 = yes, 0 = no).

• Promo2: Indicates whether the store is participating in Promo2, a continuing promotion for some stores (1 = participating, 0 = not participating).

• Promo2Since[Year/Week]: The year and calendar week when the store started participating in Promo2.

• PromoInterval: Describes the months when Promo2 is active, e.g., "Feb,May,Aug,Nov" means the promotion starts in February, May, August, and November.

Software Requirements

To work with this dataset, you will need to have specific software installed, including:

• DBRepo Authorization: This is required to access the datasets via the DBRepo API. You may need to authenticate with an API key or login credentials to retrieve the datasets.

• Python Libraries: Key libraries for working with the dataset include:

  • pandas for data manipulation,

  • numpy for numerical operations,

  • matplotlib and seaborn for data visualization,

  • scikit-learn for machine learning algorithms.

Additional Resources

Several additional resources are available for working with the dataset:

1. Presentation:
   A presentation summarizing the exploratory data analysis (EDA), feature engineering process, and key insights from the analysis is provided. This presentation also includes visualizations that help in understanding the dataset’s trends and relationships.

2. Jupyter Notebook:
   A Jupyter notebook, titled Retail_Sales_Prediction_Capstone_Project.ipynb, is provided, which details the entire machine learning pipeline, from data loading and cleaning to model training and evaluation.

3. Model Evaluation Results:
   The project includes a detailed evaluation of various machine learning models, including their performance metrics like training and testing scores, Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). This allows for a comparison of model effectiveness in forecasting sales.

4. Trained Models (.pkl files):
   The models trained during the project are saved as .pkl files. These files contain the trained machine learning models (e.g., Random Forest, Linear Regression, etc.) that can be loaded and used to make predictions without retraining the models from scratch.

5. sample_submission.csv:
   This file is a sample submission file that demonstrates the format of predictions expected when using the trained model. The sample_submission.csv contains predictions made on the test dataset using the trained Random Forest model. It provides an example of how the output should be structured for submission.

These resources provide a comprehensive guide to implementing and analyzing the sales forecasting model, helping you understand the data, methods, and results in greater detail.

Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied. We are provided with historical sales data for 1,115 Rossmann stores. The task is to forecast the "Sales" column for the test set.

This statistic displays the revenue development per store of Rossmann in Germany from 2011 to 2024. Rossmann is the one of the largest drugstore chain in Germany. Over the period in consideration, its revenue per store increased and stood at over four million euros per store as of 2024.

The online revenue of rossmann.de amounted to US$65.9m in 2024. Discover eCommerce insights, including sales development, shopping cart size, and many more.

The online revenue of rossmann.dk amounted to US$1.6m in 2024. Discover eCommerce insights, including sales development, shopping cart size, and many more.

******** is the leader in the market of drugstores. In 2024, ** percent of the respondents have recently purchased cosmetics, perfumes, or hygiene items there. Hebe followed with a share of ** percent.

The retail sector for cosmetics and personal care products has had to contend with a subdued consumer climate in recent years and is facing increasing price and predatory competition from online-only retailers. The sector is characterised by the high market power of the four major players as well as strong external competition from supermarkets, discounters and online retailers. It is characterised by intense competition, which is reflected in low profit margins. Despite the difficult market conditions, cosmetics retailers offering a wide and constantly changing range of personal care and cosmetic products as well as the associated consulting services have been able to successfully hold their own in the market. Industry sales are expected to total 24.4 billion euros in 2025. This corresponds to an increase in sales of 2.3% compared to the previous year with an average annual growth rate of 2.6% between 2020 and 2025.The biggest growth driver in the current year is increasing health and environmental awareness as well as a generally high demand for care products, even with increased savings behaviour. Demand for natural cosmetic products is increasing due to rising health and environmental awareness. In addition, the importance of decorative cosmetics is growing due to the increasing use of social media, which is significantly increasing the focus on beauty ideals. The persistently high demand for cosmetics and body care products remains stable even with increased savings behaviour, as these products fulfil several roles. They are not only used for facial and body hygiene and health care, but also have a psychological benefit. This is why these products remain particularly attractive, even if consumer confidence is dampened. Another important factor for the development of sales in this sector is the net disposable income of households. Although some sector products are everyday consumer goods, demand for non-basic consumer goods and luxury brand products depends on income. Household incomes have risen recently, which has had a positive impact on the sector's sales development. The previously predominantly female target group of cosmetics retailers is increasingly being joined by men, children and young people as new target groups.IBISWorld expects industry sales to increase at an average growth rate of 1.6% per year to €26.4 billion by 2030. This is due in particular to growth drivers in the product segments of anti-ageing skincare and natural and men's cosmetics. Nevertheless, the booming internet and mail-order business as well as supermarkets and discounters are likely to intensify price and crowding-out competition in the retail sector over the next five years and inhibit sales growth somewhat.

In 2024, most respondents in Poland bought cosmetics, perfumes, or hygiene products in-store. Rossmann was the most popular chain for in-store purchases. When it came to online purchases, Allegro had the most significant share.

In 2024, the share of purchases of cosmetics, perfumes, or hygiene items in Poland by women was higher than by men. Among women, Rossmann, Hebe, and Biedronka were the most popular. Men did their shopping mostly in Rossmann or Biedronka or did not purchase such products.

In 2023, the drugstore chain dm generated the highest gross revenue out of the four leading drug store brands in Germany, with a revenue of almost 11.4 billion euros. Rossmann followed in second place in the German market, with sales of around 9.3 billion euros that year.