As a data analyst at Diogo's Delicious Chocolate Company (DDCC), you have been assigned a time series forecasting project by your manager who heads the supply chain team. The company has been facing issues in the supply chain where some chocolates sell out too quickly, while others sit in inventory for 2 years. Therefore, DDCC is trying to optimize its production and inventory management, however, accurate forecasts of chocolate demand are crucial for this purpose. The project will start with a financial forecast for the next 3 months. Your manager has asked you to use exponential smoothing and Holt-Winters methods to develop reliable forecasts of chocolate sales for the next quarter. You will be working closely with the sales and marketing teams, who will provide you with historical sales data as well as any relevant information about promotions and other demand drivers. You are expected to deliver a report with your findings, including recommendations, to the supply chain team and a Python codebase that can be used to generate updated forecasts on a regular basis. Your manager will be looking for clear and concise communication of the methods, results, and accuracy of your forecasts.

As a member of the Analytics team at Diogo's Delicious Chocolate Company, you are being assigned a project to develop a time series forecasting model using the Holt-Winters method. The goal of this project is to accurately forecast chocolate sales for each month of the next quarter so that the company can effectively plan production and inventory management. In order to complete this project, you will need to have a strong understanding of time series analysis, as well as experience using the Holt-Winters method to train and test the model before using it to predict the next 3 months. It is also important that you have a good understanding of the business operations and target market of Diogo's Delicious Chocolate Company, as this will inform your model development. Please make sure to carefully document your process and the results, as you will be presenting your findings to the management team.

To solve a time series forecasting problem using Holt-Winters in Python, you will need to first import the necessary libraries, load the data, and visualize it to get to know it better. The data is provided in a CSV file, called choco_monthly_revenue.csv; it contains the revenues from 2018 to 2022, on a monthly basis, and it is already in the workspace ready to be loaded. You will then need to preprocess the data, including checking for missing values. You can then fit the model to the data and make predictions for the desired forecast horizon. Finally, you should evaluate the model's performance and assess the next steps.

• Implement exponential smoothing and Holt-Winters in Python using appropriate libraries
• Identify and handle seasonality in time series data using Holt-Winters
• Analyze the performance of exponential smoothing and Holt-Winters models using evaluation metrics such as mean, absolute error, and root mean squared error
• Use exponential smoothing and Holt-Winters to forecast short-term and long-term trends in time series data
• Use exponential smoothing and Holt-Winters to make informed decisions based on forecasts in business and industry settings
• Communicate the results and implications of exponential smoothing and Holt-Winters forecasts effectively to technical and non-technical audiences

• Time Series Analysis
• Time Series Visualization
• Forecasting
• Assessing Forecasting Models
• Statistical Modeling
• Holt-Winters and Exponential Smoothing