Hey, everyone, and welcome to Time series Analysis.
This is the best and most in-depth online resource for learning about how to use Python to analyze time
series data.
In this video, I'm going to give you a brief outline for what you will learn in this course.
What's interesting about this course is that pretty much every machine learning technique I have ever
taught can be applied to time series analysis.
Not only will we get a chance to study classic time series techniques, but we will also see how modern
machine learning and deep learning can be applied.
So let's start with a very high level overview of the course.
This course can be broken up into three parts.
Part one covers classic methods like Exponential Smoothing and Arima.
Part two covers modern machine learning and deep Learning.
Part three covers VIP content, which will only be available in the VIP version of the course.
Note that each of these parts can really be done in any order you please, so if you know what you're
doing, don't hesitate to skip to the lectures you care about most.
So let's go into a little more detail before we even go into time series modeling.
We're going to have an entire section devoted to time series basics.
So if you've never worked with a time series before, this is stuff you need to know.
For example, what kind of data can be considered a time series?
What kind of data transformations are useful for time series?
How do we evaluate a forecast?
What kind of metrics do we use?
And because I know a lot of you are interested in Financial Time series, that will be a major focus
of this course.
We'll go through a Financial Time series Primer, where you will be introduced to important concepts
such as log prices, log returns, and the random hypothesis.
The first classical time series method we will discuss is called Exponential Smoothing or ETS.
This kind of model is ideal for time series, which are made up of error, trend and seasonal components.
We'll apply this method to both sales data and financial time series.
The second classical method we will discuss is called ARIMA.
This kind of model more closely resembles what you would see in a machine learning.
Now, although a lot of theoretical analysis is possible, this course focuses on the practical aspects
of this technique.
For example, how to choose the arena orders, P, D, and Q.
We'll learn about tools such as the ACF, F and Auto Arima to help us do this.
We'll also look at vector models and see whether or not the interactions between multiple variables
in a time series can help us predict the future.
So after we learn about the classic Time series methods, we will then move on to machine learning.
In this section, we'll see how machine learning models like logistic regression, the support vector
machine and the random forest can be used for time series analysis.
Thanks to the flexibility of machine learning.
Most of these models will let us do both forecasting and classification.
As an example, suppose you want to predict what a user is doing based on readings from their smartphone
accelerometer.
So after discussing machine learning, we'll discuss the latest and greatest machine learning approach,
deep neural networks and these sections we'll look at and CNNs CNN's and origins.
And yes, because everyone loves LCMS, this will include LCMS.
Although you may have seen me teach these models before for different kinds of data, these can all
be used for time series analysis.
And if you think you know everything there is to know, because you've taken my deep learning courses
in the past, you will be surprised.
So if you've signed up for the VIP version of this course, you're going to get access to some very
exciting material.
So one of the VIP sections we'll look at of us forecast.
This will let you use Amazon's state of the art forecasting methods with barely any code.
In fact, you do not even have to understand time series analysis in order to use this.
As long as you know how to use Amazon's APIs and you can get your data into CSV format.
You can have access to this powerful technology.
Amazon forecast also performs AutoML and Hyper parameter optimization.
So all of the grunt work typically involved in machine learning is taken off your hands.
Another VIP section.
We'll look at the garden method.
So this is a very important model for Financial Time series, because not only do we like to model the
price over time, but also the volatility or variance, which is a measure of risk.
Another VIP section will look at Prophet Facebook's famous time series tool.
Nothing beats a free tool made by one of the largest tech companies in the world that specializes in
machine learning.
And yes, even more surprise VIP material is coming.
So stay tuned for updates.
Okay, So again, part one classical methods, Part two Machine learning and deep neural networks and
part three VIP content.
I hope you're excited for this course because I had such a fun time at putting it together.
Thanks for listening and I'll see you in the next lecture.