Topic 13 – Retail Store Sales Time Series (Kaggle Competition Data)

Dataset

• Source: Store Sales – Time Series Forecasting (Favorita) – Kaggle
• Link: https://www.kaggle.com/competitions/store-sales-time-series-forecasting/data

Download Instructions

1. Open the competition link above.
2. Go to "Data" tab.
3. Accept competition rules (if needed).
4. Download train.csv and save to data/store_sales/.

Data Loading

import pandas as pd
df = pd.read_csv("data/store_sales/train.csv")
df["date"] = pd.to_datetime(df["date"])
df = df.set_index("date").sort_index()
print(df.head())
Columns usually include: date, store_nbr, family, sales, onpromotion, oil_price, etc.

Implementation Steps

1. Data Exploration

• Load training data
• Understand data structure:
• Multiple stores
• Multiple product families
• Time series panel data
• Inspect available features (promotions, oil prices, holidays, etc.)
• Examine data quality and missing values

2. Exploratory Data Analysis (EDA)

• Aggregate sales by store, family, and time
• Plot overall sales trends
• Analyze seasonality patterns (daily, weekly, monthly, yearly)
• Identify holiday effects
• Analyze promotion effects
• Examine relationships between features
• Calculate sales statistics by store/family

3. Data Preprocessing

• Handle missing values
• Create time-based features (day of week, month, year, holidays)
• Encode categorical variables (store, family)
• Handle promotions and external variables
• Prepare panel data structure

4. Feature Engineering

• Time Features:
• Day of week, month, year
• Holiday indicators
• Lag features (previous day, week, month sales)
• Rolling Statistics:
• Rolling mean, std (7-day, 30-day windows)
• Rolling max, min
• External Features:
• Oil prices
• Promotion indicators
• Store/family characteristics

5. Model Building

• Univariate Approaches (per store-family):
• ARIMA/SARIMA for individual series
• Exponential smoothing
• Panel Data Approaches:
• Hierarchical models
• Store/family-specific models
• Machine Learning:
• Feature-based models (Random Forest, XGBoost, LightGBM)
• Handle panel structure appropriately
• Hybrid Approaches:
• Combine classical and ML methods
• Ensemble forecasts

6. Model Evaluation

• Use time series cross-validation (walk-forward)
• Split data temporally (respect time ordering)
• Calculate metrics (MAE, RMSE, MAPE)
• Evaluate at different aggregation levels (store, family, overall)
• Compare multiple approaches

7. Forecasting

• Generate forecasts for test period
• Aggregate forecasts appropriately
• Handle multiple stores/families
• Include uncertainty estimates
• Visualize forecasts

8. Advanced Analysis (Optional)

• Analyze store/family-specific patterns
• Identify important features
• Analyze promotion effectiveness
• Compare store performance
• Seasonal pattern analysis

Expected Deliverables

1. EDA Report:

• Sales trends and patterns
• Seasonality analysis
• Feature analysis
• Store/family comparisons

1. Model Results:

• Selected model(s) with parameters
• Performance metrics (by store/family and overall)
• Feature importance (if using ML)
• Forecast plots

1. Code:

• Complete Python notebook
• Functions for data processing
• Feature engineering utilities
• Modeling pipeline

Tips

• This is panel data - handle multiple stores/families appropriately
• Strong seasonality expected (weekly, monthly, yearly patterns)
• Holidays and promotions have significant effects
• Use appropriate aggregation levels for analysis
• Feature engineering is crucial for good performance
• Consider hierarchical models (store → family → item)
• Use time series cross-validation (don't shuffle time series data)
• Handle missing values carefully (forward fill, interpolation)
• External features (oil prices, promotions) can improve forecasts
• Consider store/family-specific models vs global models
• This is competition data - aim for good performance
• Document all feature engineering decisions
• Compare simple models (ARIMA) with complex models (XGBoost)