Hi, welcome to the Time Series Forecasting course.
Before we dive into any lessons,
let's examine what exactly time series forecasting is.
Well, to put it simply, time series forecasting is the use of a statistical
model to predict future values, based on past results.
So what kinds of variables can we forecast?
Any value that can be tracked and collected over time.
Think of annual population data, a company's daily stock price or
quarterly sales figures.
For each of these examples, data is collected over time and
the time series model is simply using that data to forecast future values.
During the course, we'll answer questions like how do I define time?
Or what are trends?
[LAUGH] Not that kind of trend.
And what are seasonal patterns?
We'll also dig into how to use particular forecasting models
like an Exponential Smoothing or ETS,
and an Autoregressive Integrated Moving Average or ARIMA models.
Along the way we will be working through a specific business problem
in Alteryx designer to examine these topics in more detail.
Now wait.
Do you need to be an expert in all Alteryx prior to this course?
Not at all.
Luckily, you've been working with our pros here at Alteryx in previous courses
and I'll help continue painting your analytical picture on the designer
canvas.
But first, let's briefly look at where time series forecasting fits
relative to other types of analysis.
In the Problem Solving with Advanced Analytics course, you're introduced to
Methodology Map which is used to determine which methodology is best for
a particular analysis.
So, with our business problem, we need to identify if we're doing data analysis
or trying to predict an outcome.
This one's easy,
we're trying to predict future values based on past observations.
Next, we'll continue along in the Data Rich branch and
into our Numeric family of models.
The time series will need a numeric variable that we'd like to predict.
This is our target variable.
And it also needs a time based variable.
Think about our examples.
Year over year population, a stock's daily closing price and
lastly, our quarterly sales figures.
All of this are numeric variables.
The data for these examples are collected by year, day, and
quarter, respectively.
So each observation has a time component to it.
If the business problem involves predicting these values into the future,
then time series forecasting models like ARIMA and
ETS models will be most appropriate.