Скачать или смотреть Performance Evaluation in Forecasting | MAE, RMSE, MAPE & MASE Explained with Examples

In this video, we learn how to evaluate forecasting models using the most important performance metrics in Data Science and Time Series Analysis.

After building models like ARIMA or SARIMA, we must measure how accurate the predictions are. This is done using forecasting error metrics.

📊 Metrics Covered in This Video:

1️⃣ Mean Absolute Error (MAE)
2️⃣ Root Mean Squared Error (RMSE)
3️⃣ Mean Absolute Percentage Error (MAPE)
4️⃣ Mean Absolute Scaled Error (MASE)

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🔎 What You Will Learn:

✔ Clear definition and formula of each metric
✔ Step-by-step numerical examples
✔ Interpretation of results
✔ When to use MAE vs RMSE
✔ Why MAPE can fail when actual values are near zero
✔ How MASE compares models to a naive forecast
✔ Practical understanding for exams and real-world forecasting

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📌 Why This Topic Is Important:

✔ Essential for Time Series Forecasting
✔ Frequently asked in university exams
✔ Important for interviews and practical projects
✔ Helps select the best forecasting model

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This video is ideal for:

• Data Science students
• Time Series beginners
• ARIMA / SARIMA learners
• Exam preparation (conceptual clarity)
• Anyone working with forecasting models

📈 Watch till the end for a clear comparison table and metric selection guide.

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