Time Series Forecasting
Project Overview:
For this Kaggle project we used time-series forecasting to forecast store sales based on historical data for large Ecuadorian-based grocery retailer.
Objective:
To acquire and deepen our expertise in Time Series Analysis projects.
Steps taken to complete the project:
Data Understanding
Familiarized with the dataset, including stores, items, sales as well as the price of oil and how it altered during the years of interest (2013-2017). We also showcased the impact of holidays on the sales
Data Cleaning
Addressed missing values, outliers, and ensured data consistence
Exploratory Data Analysis (EDA)
Analyzed data trends, seasonality, and correlations
Feature Engineering
Created new features such as lagged sales, moving averages, holidays, and promotional indicators
Model Selection
Evaluated multiple models including ARIMA, Facebook Prophet, and machine learning models like XGBoost and LSTM
Model Training and Tuning
Split data into training and validation sets; performed hyperparameter tuning.
Model Evaluation
Assessed models using metrics like RMSE and MAPE
Final Model Deployment
Selected the best model and retrained on the full dataset for final forecasting
Outcomes:
Achieved a significant reduction in forecast error
Developed a robust model capable of adapting to new data inputs
Provided actionable insights on sales trends and contributing factors
Technologies Used:
Data Processing
Python, Pandas, NumPy
Visualization
Matplotlib, Seaborn
Time-Series Analysis
Statsmodels, Facebook Prophet
Machine Learning Models
XGBoost, LightGBM, TensorFlow/Keras for LSTM
Model Evaluation
scikit-learn
Deployment
Flask
Project Results and Impact:
Delivered accurate sales forecasts that enabled better inventory management and strategic planning for the retailer
Demonstrated the capability of advanced analytics and machine learning in driving business decisions
Enhanced the client's ability to anticipate market demand and optimize resource allocation
What we learned from the project:
Data Quality is Crucial
High-quality data significantly impacts the accuracy of forecasting models
Feature Engineering Enhances Model Performance
Custom features derived from domain knowledge can provide a competitive edge.
Model Flexibility
Combining multiple models and techniques can lead to more robust solutions
Continuous Learning and Adaptation
The model's accuracy can be maintained and improved over time with regular updates and by incorporating new data
Client Communication
Clearly communicating the insights and actionable recommendations is essential for client satisfaction and value realization.
Conclusion:
The project showcased the effectiveness of using a combination of traditional time-series techniques and advanced machine learning models for sales forecasting.
It reinforced the importance of thorough data analysis and feature engineering in improving model accuracy.
The successful deployment of the forecasting model provided the retailer with a powerful tool to better manage their operations and strategic initiatives.
