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--- |
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title: TurnoverForecasting |
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emoji: 📊 |
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colorFrom: blue |
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colorTo: red |
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sdk: gradio |
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sdk_version: 5.22.0 |
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app_file: app.py |
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pinned: false |
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license: mit |
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short_description: Forecasting SAP SE Revenue with AI |
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--- |
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# 📊 AI-Powered Turnover Forecasting for SAP SE |
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## 🚀 Project Overview |
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This project delivers **AI-driven revenue forecasting** for **SAP SE** using a **univariate SARIMA model**. |
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It shows how accurate forecasts can be built from limited data (just historical turnover). |
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--- |
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## 🏢 Why SAP SE? |
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- SAP SE is a **global leader in enterprise software** |
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- Revenue forecasts support **strategic planning & growth** |
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- Perfect case for **AI-powered financial forecasting** |
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--- |
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## 🧠 Model Details |
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- **Model type**: SARIMA (Seasonal ARIMA) |
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- **Trained on**: SAP SE revenue from Top 12 German Companies Dataset (Kaggle) |
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- **SARIMA Order**: (3, 1, 5) |
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- **Seasonal Order**: (0, 1, 0, 12) |
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- **Evaluation Metric**: MAE (Mean Absolute Error) |
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- **Validation**: Walk-forward validation with test set (last 10%) |
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--- |
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## ⚙️ How to Use |
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```python |
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import pickle |
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with open("sarima_sap_model.pkl", "rb") as f: |
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model = pickle.load(f) |
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forecast = model.forecast(steps=4) |
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print(forecast) |
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``` |
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## 📌 Intended Use & Limitations |
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👍 Forecast SAP SE revenue for next 1–6 quarters |
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📈 Great for univariate, seasonal time series |
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🚫 Not suitable for multivariate or non-seasonal data |
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⚠️ Requires careful preprocessing (e.g., stationarity) |
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👨💻 Author: Pranav Sharma |
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