PranavSharma/turnover-forecasting-model

📊 AI-Powered Turnover Forecasting for SAP SE

🚀 Project Overview

This project delivers AI-driven revenue forecasting for SAP SE using a univariate SARIMA model. It shows how accurate forecasts can be built from limited data (just historical turnover).

🏢 Why SAP SE?

• —SAP SE is a global leader in enterprise software
• —Revenue forecasts support strategic planning & growth
• —Perfect case for AI-powered financial forecasting

🧠 Model Details

• —Model type: SARIMA (Seasonal ARIMA)
• —Trained on: SAP SE revenue from Top 12 German Companies Dataset (Kaggle)
• —SARIMA Order: (3, 1, 5)
• —Seasonal Order: (0, 1, 0, 12)
• —Evaluation Metric: MAE (Mean Absolute Error)
• —Validation: Walk-forward validation with test set (last 10%)

⚙️ How to Use

import pickle

with open("sarima_sap_model.pkl", "rb") as f:
    model = pickle.load(f)

forecast = model.forecast(steps=4)
print(forecast)

📌 Intended Use & Limitations

👍 Forecast SAP SE revenue for next 1–6 quarters 📈 Great for univariate, seasonal time series 🚫 Not suitable for multivariate or non-seasonal data ⚠️ Requires careful preprocessing (e.g., stationarity)

👨‍💻 Author: Pranav Sharma