Measuring Forcast Accuracy using Mean Error, MAE, MSE, MPE, MAPE

In this video, we’ll dive into the world of time series analysis by learning how to measure the accuracy of forecasts using key metrics. Watch as we walk through the calculations step-by-step, using a small dataset to explain:

Mean Error (ME): Understand forecast bias and whether our predictions tend to be over or under the actual values.
Mean Absolute Error (MAE): Learn how much our forecasts deviate from actual observations, regardless of direction.
Mean Squared Error (MSE): Discover how this metric emphasizes larger errors by squaring the deviations.
Percentage Errors (MPE and MAPE): Calculate the average percentage deviation of forecasts from actual values, in both raw and absolute terms.

Chapters
01:20 - Mean Error
02:06 - Mean Absolute Error
02:55 - Mean Squared Error
03:50 - Mean Percentage Error
05:40 - Mean Absolute Percentage Error


We’ll use a simple dataset from 2000 to 2004 to illustrate each calculation clearly and effectively, making it easy for you to understand these essential forecasting tools. Whether you're new to forecasting or just need a refresher, this video will help you master these concepts and improve your predictions!

Fitted erroe and Forcasting Error Video link - https://youtu.be/00sUXVhxGbE

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