Introduction to Time Series Forecasting | SCMT 3623

Summary

This video introduces the basics of time series forecasting, focusing on three fundamental methods: the last period demand (or naive) forecasting, the simple average forecasting, and the moving average forecasting methods. Using historical sales data, the video illustrates how each technique works, explaining the calculation process for each method. Examples are given, such as forecasting week 53 demand based on week 52 data. The video concludes with questions about determining the best forecasting method and how to implement them in practice, setting the stage for further exploration of time series forecasting.

Takeaways

• 🕒 The video introduces time series forecasting, a method that uses historical sales data to predict future demand.
• 📊 Three basic time series forecasting methods are discussed: last period demand, simple average, and moving average forecasting techniques.
• 🔢 The last period demand forecasting method assumes the forecast for a given period will be equal to the actual demand of the preceding period.
• 📉 This method is simple and naive, often referred to as the naive forecasting method, and is illustrated with a dataset of 52 weekly demand observations.
• 🔄 The simple average forecasting method calculates the forecast based on the average of all historical demand observations.
• 📊 For the dataset spanning 52 weeks, the simple average forecast for the next period is the average of the past 52 weeks, which is 512 units.
• 🌊 The moving average forecasting method uses the average of a set number of recent demand observations to make a forecast.
• 📈 In the example given, a four-week moving average forecast is calculated, averaging the demand observations from weeks 49 through 52 to predict week 53.
• 🤔 The video raises questions about which forecasting method is the best, how to choose the right one, and how to implement these methods in practice.
• 🏁 The objectives of the module include finding answers to the questions raised about the effectiveness and application of different forecasting methods.

Q & A

What is time series forecasting?

Time series forecasting is the process of using historical data to predict future demand or trends by analyzing past patterns in data, such as sales, over time.

What are the three basic time series forecasting methods mentioned in the video?

The three basic time series forecasting methods mentioned are: (1) Last Period Demand Forecasting, (2) Simple Average Forecasting, and (3) Moving Average Forecasting.

How does the Last Period Demand Forecasting method work?

The Last Period Demand Forecasting method predicts future demand by assuming that the forecast for a given period will be equal to the actual demand of the preceding period.

What is an example of Last Period Demand Forecasting?

An example is when forecasting for week 53: according to the Last Period Demand method, the forecast will be equal to the actual demand in week 52, which was 436 units.

Why is the Last Period Demand method also called the 'naive' forecasting method?

It is called the 'naive' forecasting method because it assumes that the future will be exactly the same as the most recent past, without considering any other factors or trends.

What is the Simple Average Forecasting method?

The Simple Average Forecasting method calculates the forecast by taking the average of all past demand observations, providing a more generalized prediction based on historical data.

What is the forecast for week 53 using the Simple Average method?

Using the Simple Average method, the forecast for week 53 is 512 units, which is the average weekly sales volume over the past 52 weeks.

How does the Moving Average Forecasting method work?

The Moving Average Forecasting method calculates the forecast by taking the average of a specific number of preceding periods. For example, a four-week moving average would average the demand from the previous four weeks to predict the next week.

What is the four-week moving average forecast for week 53?

The four-week moving average forecast for week 53 is 413 units, calculated from the average of demand from weeks 49 through 52.

What important questions arise from using different forecasting methods?

The important questions are: (1) Which is the best forecasting method? (2) How can we choose the right forecasting method? (3) How do we implement these forecasting methods in practice?