Time series forecasting is a common task in machine learning that involves predicting future values of a time series based on its historical values. Promotional forecasting, in particular, is important for businesses to optimize promotional strategies and allocate resources effectively. Machine learning algorithms, such as ARIMA or LSTM, can provide a solution by modeling the complex relationships between promotional events and sales data.
In this project, we aim to use machine learning algorithms to forecast sales data during promotional events. The proposed workflow for the Promotional Time Series project includes the following steps:
- Data Collection and Preprocessing: We will collect a dataset of sales data and promotional events, such as discounts or promotions, and preprocess it by cleaning and normalizing the data, removing outliers, and performing feature selection and engineering.
- Feature Selection and Engineering: We will select a subset of relevant features from the dataset, such as the type of promotional event, the time of year, and the product category. We will also engineer new features, such as the frequency of promotions or the impact of weather on sales, to improve the model’s performance.
- Model Training and Selection: We will train a machine learning model, such as ARIMA or LSTM, on the preprocessed dataset. The model will forecast future sales data during promotional events. We will evaluate the performance of the model using metrics such as mean squared error (MSE) or root mean squared error (RMSE).
- Model Evaluation and Analysis: We will evaluate the performance of the machine learning model using cross-validation and backtesting techniques. We will also analyze the factors that contribute to the accuracy of the model, such as the length of the promotional event or the timing of the forecast.
- Model Deployment and Integration: We will deploy the machine learning model to a cloud-based platform or desktop application, which can provide real-time sales forecasts during promotional events based on the input features. We will also integrate the model into existing systems, such as inventory management or pricing optimization tools.
The expected outcomes of this project include a scalable and efficient machine learning algorithm for promotional time series forecasting, a comprehensive sales and promotional events dataset, and a set of best practices and guidelines for applying machine learning algorithms to sales forecasting. The project has numerous applications, including sales and marketing strategy, inventory management, and resource allocation. The insights gained from this project can also inform decision-making in other domains, such as financial forecasting or demand planning.
