[](https://youtu.be/mAv1SEXUKhE "Introduction to Time Series Forecasting")
In this lesson and the following one, you will learn a bit about Time Series Forecasting, an interesting and valuable part of a ML scientist's repertoire that is a bit lesser known than other topics. Time Series Forecasting is a sort of crystal ball: based on past performance of a variable such as price, you can predict its future potential value. It's a powerful and interesting field especially in business, unsurprisingly, given its direct application to problems of value. While deep learning techniques have started to be used to gain more insights in the prediction of future performance, it remains a field greatly informed by classic ML techniques.
Supposing you maintain an array of smart parking meters that provide data about how often they are used and for how long over time. What if you could generate revenue to maintain your streets by tweaking the prices of the meters when there is greater demand for them? What if you could predict, based on the meter's past performance, its future value according to the laws of supply and demand? This is a challenge that could be tackled by a Time Series problem. It wouldn't make those in search of a rare parking spot in busy times very happy to have to pay more for it, but it would be a sure way to generate revenue to clean the streets!
Let's explore some of the types of Time Series algorithms and start a notebook in preparation of cleaning some data. The data you will analyze is taken from the GEFCom2014 forecasting competition. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. Given the historical patterns of electricity load and temperature, you can predict future values of electricity load. In this example, you'll learn how to forecast one time step ahead, using historical load data only.
