Sales and Inventory Forecasting Practice: Time Series Modeling with Taobao API

1. Why Is Sales Forecasting Important?

In e-commerce operations, fluctuations in sales and inventory directly affect revenue, cost management, and customer satisfaction. Establishing an accurate sales forecasting system can help businesses:

• Predict hot-selling products and stock them in advance to avoid stockouts

• Control excess inventory of unsold items, reducing warehousing and capital pressure

• Quantify the impact of promotions on sales to optimize campaign timing and strategy

• Create precise stocking and distribution plans for holidays or cross-border sales peaks

• Strengthen decision-making through data-driven insights, improving overall operational efficiency

By integrating historical data with intelligent forecasting models, merchants can gain a forward-looking advantage and enhance competitiveness.

2. Data Collection: Using Taobao API to Fetch Sales and Inventory Data

Forecasting relies on accurate historical data. Regularly collect the following key information from the Taobao API (or via legal web scraping if permitted):

• Product ID and name

• Date (daily or hourly granularity)

• Daily sales and cumulative sales

• Current inventory (if supported by the API)

Here’s an example of a simplified API request (assuming a wrapped service):

import requests
import pandas as pd
url = "https://api.example.com/taobao/sales"
params = {"product_id": "12345678", "start_date": "2024-01-01", "end_date": "2024-04-30"}
headers = {"Authorization": "Bearer YOUR_API_KEY"}
res = requests.get(url, headers=headers, params=params)
data = res.json()["data"]
df = pd.DataFrame(data)
df["date"] = pd.to_datetime(df["date"])
df.set_index("date", inplace=True)
df = df.sort_index()
print(df.head())

Standardized data preparation ensures a solid foundation for modeling.

3. Building a Sales Forecast Model with Facebook Prophet (Beginner Friendly)

Prophet is an open-source time series forecasting tool by Meta that supports missing data, holiday effects, and seasonalities. It’s ideal for those new to time series modeling.

1. Installation and Basic Modeling

Install the Prophet package:

pip install prophet

Build your model using historical sales data:

from prophet import Prophet
# Prepare data format
df_prophet = df.reset_index().rename(columns={"date": "ds", "sales": "y"})
model = Prophet()
model.fit(df_prophet)
future = model.make_future_dataframe(periods=30)  # Forecast 30 days into the future
forecast = model.predict(future)
# Visualize the forecast
model.plot(forecast)

The output shows trends, uncertainty intervals, and seasonal patterns.

2. Incorporating Holiday Effects

Holidays like Double 11 and Chinese New Year significantly impact sales. You can include them in the model to improve accuracy:

holidays = pd.DataFrame({
"holiday": "double11",
"ds": pd.to_datetime(["2024-11-11", "2025-11-11"]),
"lower_window": -1,
"upper_window": 1,
})
model = Prophet(holidays=holidays)

The model will learn holiday-driven spikes and adapt its predictions accordingly.

4. Advanced Modeling: LSTM Deep Learning for Sales Forecasting

If your sales data has complex trends, irregular seasonality, or frequent anomalies, LSTM (Long Short-Term Memory networks) can better capture sequential dependencies.

1. Data Preprocessing and Sequence Creation

from sklearn.preprocessing import MinMaxScaler
import numpy as np
scaler = MinMaxScaler()
scaled = scaler.fit_transform(df[["sales"]].values)
def create_dataset(data, look_back=7):
X, y = [], []
for i in range(len(data) - look_back):
X.append(data[i:i+look_back])
y.append(data[i+look_back])
return np.array(X), np.array(y)
X, y = create_dataset(scaled)
X = X.reshape((X.shape[0], X.shape[1], 1))

You can adjust the look_back parameter to define how many days of historical data the model uses for prediction.

2. Building and Training the LSTM Model

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
model = Sequential([
LSTM(64, input_shape=(X.shape[1], 1)),
Dense(1)
])
model.compile(loss="mse", optimizer="adam")
model.fit(X, y, epochs=20, batch_size=16)

Customize the number of neurons, epochs, and batch size to optimize model performance based on your dataset size.

5. Forecasting in Action: Inventory Alerts and Visualization

Forecasts should inform business decisions. Here's a simple example of a stock alert based on predicted demand:

predicted_sales = model.predict(X[-1].reshape(1, -1, 1))
future_demand = scaler.inverse_transform(predicted_sales)[0][0]
current_stock = 200
if current_stock < future_demand:
print("✅ Inventory Alert: Forecasted demand exceeds current stock. Consider restocking.")
else:
print("✅ Stock is sufficient. No restocking needed.")

You can also build a web dashboard to display sales trends, inventory levels, forecast results, and alerts, providing real-time insights for decision-makers.

6. Conclusion: Build Your Own Smart Forecasting System

From data collection and model training to real-time forecasting applications, creating a practical e-commerce sales forecasting system is within reach. Depending on your needs, use Prophet for quick deployment or LSTM for more complex patterns. You can further enhance your system with:

• Batch forecasting for multiple products

• Regional demand forecasting by store location

• Anomaly detection and alerts for unusual sales activity

• Dynamic adjustment of ad budgets and inventory strategy

With these technologies and integrations, you’ll gain better control over operations, respond quickly to market changes, and increase profitability through intelligent forecasting.

Articles related to APIs :

• Building an Interactive Reporting and Visualization Platform: A Practical Guide to Unlocking the Value of Taobao Data

• Building an Efficient Product Search Interface: Designing and Implementing a Smart Query System with Elasticsearch

• Product and Price Monitoring System: Building an Automated Alert and Notification Workflow

• Real-Time Inventory Synchronization: A Full Implementation of Taobao Inventory API

• Utilizing Marketing APIs: Automating Coupon and Campaign Management

• Anomaly Monitoring and Automated Alerts: Intelligent Early Warning System for Taobao Product Price and Inventory Changes

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