1. Introduction to Time Series Forecasting

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Time Series Analysis

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In this lesson, we introduce the concept of a time series forecast. We will look at the differences between time series analysis and regression analysis.


  1. Lesson Goal (00:11)

    The goal of this lesson is to understand the concept of time series forecasting.

  2. Understanding Time Series Data (00:18)

    Time series data refers to any single variable that has been observed over a period, indexed in time order. Our goal when analyzing time series data is to create a forecast, which is a projection of future values for the time series variable, based on the historical data.

    The accuracy of a forecast depends on how much historical data is available, and how completely we understand what influences the results. As a result, the degree of confidence of a forecast can be more important than the forecast itself.

    Time series forecasting has some similarities with regression analysis. Time series forecasting uses past values of a single variable to predict future values of the same variable, while regression uses the values of many variables at a single point in time to predict the value of another variable at the same point in time.

    One important assumption made in time series forecasting is that the factors influencing a time series variable will be constant over time. In other words, factors affecting a variable in the past will affect the variable in the same way in the future. All the models we cover in this course make this assumption.