The Naive forecast is an extremely simple approach to forecasting future values from a set of observations – you just use the previous time period’s observation to obtain the forecast for the next period. Using a past exam question I cover the method in this question and measure its accuracy using forecast errors, the Mean Absolute Error (MAE), and the Mean Absolute Percent Error (MAPE).

# Chapter 6: Forecasting

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## Lessons

## Question 1 | Naive Forecasting Method, MAE, MAPE (Preview)

## Question 2ab | Exponential Smoothing (Preview)

The data in a series of observations taken over time (a time series) can have a lot of variation. Variation leads to unreliable forecasts. To counter this we can use smoothing methods. This question introduces two of them: Exponential Smoothing and the Weighted Moving Average. MAE and MAPE are used to compare their accuracy – plus I’ll show you a third measure of accuracy that sometimes appears on tests: the Mean Squared Error (MSE).

## Question #2: Weighted Moving Averages (Preview)

In the previous video we were simply given the value of the Weighted Moving Average as part of the question (Question #2b). Here’s how that value was calculated from the raw data, and how the related MSE was determined from the forecast errors. The Weighted Moving Average appears on a lot of past tests, so it’s important that you can work it out for yourself.

## Question 2c | Seasonality with Trend (Preview)

Remember – Large amounts of variation in the time series data makes forecasting with accuracy difficult to achieve. Some forms of variation (like seasonal) are not random, but rather follow regular patterns that can be removed from the data to increase the reliability of our forecasts. This video shows you how to create a seasonal forecast model from data with seasonal variations and how to use it to make forecasts for time periods falling in different seasons.

## Question 3 | Linear Trend Projection (Preview)

Good News… You already know this stuff! Linear Trend Projection is no different from the linear regression method you learned in Chapter 16 at the beginning of the course. The only thing to keep in mind is that instead of using x and y in the formulas, we replace them to reflect our focus on time series data: x = t (time period), and y = Ft (the forecast value for that time period).

## Multiple Choice (Preview)

Test your understanding of Chapter 6: Forecasting. Each question is accompanied by a mini-video lecture showing you how I decided which solution was the correct one.