Data Science Interview Question : Evaluating Forecasting models

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In this video, we delve into the essential metrics used to evaluate the accuracy of time series forecasting models. Understanding these metrics is key to assessing the performance of your models and making informed decisions in data science projects.
We start by explaining fundamental error metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), highlighting their uses and how they measure different aspects of forecasting accuracy. Then, we explore advanced metrics such as Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (sMAPE) and R Squared which offer nuanced insights into model performance across various contexts.
We break down the pros and cons of each metric and guide you on when to use them for optimal results in your time series analysis.
This video provides a comprehensive overview of the tools you need to evaluate your time series models effectively, whether you're preparing for a data science interview or enhancing your machine learning expertise. By the end, you'll be equipped with the knowledge to choose the right metrics for your specific forecasting challenges.
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