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1. Introduction to Time Series Forecasting
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.
Lesson Goal (00:11)
The goal of this lesson is to understand the concept of time series forecasting.
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.