Data Ingestion and Preprocessing

Handling diverse encoding schemes is essential when processing international logistics datasets:

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.stattools import adfuller
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

# Robust file reading with encoding fallback
try:
    raw_data = pd.read_csv('./data/shipping_records.csv', encoding='utf-8')
except UnicodeDecodeError:
    raw_data = pd.read_csv('./data/shipping_records.csv', encoding='gbk')

# Temporal indexing and sorting
raw_data['timestamp'] = pd.to_datetime(raw_data['timestamp'])
chronological_data = raw_data.sort_values(by=['facility_id', 'timestamp'])

Exploratory Time Series Visualization

Visualizing throughput patterns across multiple distribution hubs identifies seasonal trends:

plt.figure(figsize=(12, 6))
for hub in chronological_data['facility_id'].unique():
    subset = chronological_data[chronological_data['facility_id'] == hub]
    plt.plot(subset['timestamp'], subset['parcel_volume'], 
             marker='o', linewidth=2, markersize=4, label=hub)

plt.xlabel('Date')
plt.ylabel('Daily Parcel Volume')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

Stationarity and Correlation Analysis

Examining autocorrelation structures informs model parameterization:

# Select specific facility for detailed diagnostics
single_facility = chronological_data[chronological_data['facility_id'] == 'Hub_A']
single_facility.set_index('timestamp', inplace=True)

fig, axes = plt.subplots(1, 2, figsize=(14, 4))
plot_acf(single_facility['parcel_volume'], lags=21, ax=axes[0])
plot_pacf(single_facility['parcel_volume'], lags=21, ax=axes[1], method='ywm')
plt.tight_layout()
plt.show()

Multi-Hub Forecasting with Exogenous Variables

Integrating promotional calendars as exteranl regressors captures demand spikes:

forecast_results = []
hubs = chronological_data['facility_id'].unique()

for hub_id in hubs:
    hub_data = chronological_data[chronological_data['facility_id'] == hub_id].copy()
    hub_data.set_index('timestamp', inplace=True)
    
    # Configure SARIMAX with promotional event indicators
    model = SARIMAX(
        hub_data['parcel_volume'],
        exog=hub_data['promotion_active'],
        order=(2, 1, 2),
        seasonal_order=(1, 1, 1, 7)
    )
    fitted = model.fit(disp=False)
    
    # Project 30 days ahead
    future_dates = pd.date_range(
        start=hub_data.index[-1] + pd.Timedelta(days=1),
        periods=30,
        freq='D'
    )
    
    # Generate baseline scenario (no promotions)
    exog_future = pd.DataFrame(
        {'promotion_active': np.zeros(30)},
        index=future_dates
    )
    
    predictions = fitted.forecast(steps=30, exog=exog_future)
    
    forecast_df = pd.DataFrame({
        'facility_id': hub_id,
        'projected_date': future_dates,
        'forecasted_volume': predictions.values
    })
    forecast_results.append(forecast_df)

# Consolidate predictions across network
master_forecast = pd.concat(forecast_results, ignore_index=True)