# 1. Introduction to Time Series Forecasting

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# Time Series Analysis

## 10 lessons , 0 exercises

Overview

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.

Summary

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.

Transcript

Time Series Analysis

Contents

02:40

04:22

06:04

05:51

03:46

05:46

06:26

06:13

05:36

#### 10. Running a Covariate Forecast

09:49

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