Forecasting models: Knowledge check

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Lesson 10 / Forecasting models0%

1. Watch the video2. Complete the test

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1 What is the main purpose of a forecasting model? To assign each email to a category such as spam or legitimate. To use historical patterns to make informed predictions about the future. To store historical observations without making predictions.

2 Which four components does the video identify in time series data? Inputs, outputs, class labels and decision boundaries. Training, installation, licensing and deployment. Trend, seasonality, long cycles and random noise.

3 How do the random forest and gradient boosting approaches described in the video handle forecasting? They treat it as a regression task using features such as yesterday's value, last week's value and the month of the year. They ignore historical values and use only the name of the data set. They require the user to provide the future value as an input.

4 Why should forecasts support better decisions rather than be expected to be perfect? Forecasting models cannot learn any structure from historical data. The future is uncertain, and random noise cannot be predicted reliably. A forecasting model always repeats the last observation without learning.

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