[](https://youtu.be/wGUV_XqchbE "Introduction to Time Series Forecasting")
## [Pre-lecture quiz](link-to-quiz-app)
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.
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.
[](https://youtu.be/wGUV_XqchbE "Introduction to Time Series Forecasting")
## [Pre-lecture quiz](link-to-quiz-app)
It's a powerful and interesting field especially in business, given its direct application to problems of value, pricing, inventory, and supply chain issues. While deep learning techniques have started to be used to gain more insights in the prediction of future performance, Time Series Forecasting remains a field greatly informed by classic ML techniques.
> Penn State's useful Time Series curriculum can be found [here](https://online.stat.psu.edu/stat510/lesson/1)
### Introduction
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 slightly augmenting 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 Time Series Forecasting. It wouldn't make those folks 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!
@ -19,14 +20,17 @@ Before starting, however, it's useful to understand what's going on behind the s
## Some Definitions
When encountering the term 'time series' you need to understand its use in several different contexts.
### Time Series
In mathematics, "a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time." An example of a time series is the daily closing value of the [Dow Jones Industrial Average](https://wikipedia.org/wiki/Time_series). The use of time series plots and statistical modeling is frequently encountered in signal processing, weather forecasting, earthquake prediction, and other fields where events occur and data points can be plotted over time.
### Time Series Analysis
Time Series Analysis is the analysis of the above mentioned time series data. Time series data can take distinct forms, including 'interrupted time series' which detects patterns in a time series' evolution before and after an interrupting event. The type of analysis needed for the time series depends on the nature of the data. Time series data itself can take the form of series of numbers or characters.
The analysis be performed using a variety of methods, including frequency-domain and time-domain, linear and nonlinear, and more. [Learn more](https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4.htm) about the may ways to analyze this type of data.
### Time Series Forecasting
Time Series Forecasting is the use of a model to predict future values based on patterns displayed by previously gathered data as it occurred in the past. While it is possible to use regression models to explore time series data, with time indices as x variables on a plot, this type of data is best analyzed using special types of models.
@ -55,11 +59,12 @@ In the next lesson, you will build an ARIMA model using [Univariate Time Series]
| 330.97 | 1975.96 | 1975 | 12 |
✅ Identify the variable that changes over time in this dataset
## Time Series [data characteristics](https://online.stat.psu.edu/stat510/lesson/1/1.1) to consider
When looking at time series data, you might notice that it has certain characteristics that you need to take into account and mitigate to better understand its patterns. If you consider time series data as potentially providing a 'signal' that you want to analyze, these characteristics can be thought of as 'noise'. You often will need to reduce this 'noise' by offsetting some of these characteristics using some statistical techniques.
### Trends
Measurable increases and decreases over time
Measurable increases and decreases over time. [Read more](https://machinelearningmastery.com/time-series-trends-in-python) about how to use and, if necessary, remove trends from your time series.
Periodic fluctuations, such as holiday rushes that might affect sales, for example. [Take a look](https://itl.nist.gov/div898/handbook/pmc/section4/pmc443.htm) at how different types of plots display seasonality in data.
Jen Looper
3 years ago
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GitHub