English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 97 lectures (7h 21m) | 1.77 GB
Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting
How does a commercial bank forecast the expected performance of their loan portfolio?
Or how does an investment manager estimate a stock portfolio’s risk?
Which are the quantitative methods used to predict real-estate properties?
If there is some time dependency, then you know it – the answer is: time series analysis.
This course will teach you the practical skills that would allow you to land a job as a quantitative finance analyst, a data analyst or a data scientist.
In no time, you will acquire the fundamental skills that will enable you to perform complicated time series analysis directly applicable in practice. We have created a time series course that is not only timeless but also:
- Easy to understand
- Comprehensive
- Practical
- To the poin
- Packed with plenty of exercises and resources
But we know that may not be enough.
We take the most prominent tools and implement them through Python – the most popular programming language right now. With that in mind…
Welcome to Time Series Analysis in Python!
The big question in taking an online course is what to expect. And we’ve made sure that you are provided with everything you need to become proficient in time series analysis.
We start by exploring the fundamental time series theory to help you understand the modeling that comes afterwards.
Then throughout the course, we will work with a number of Python libraries, providing you with a complete training. We will use the powerful time series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, StatsModels, yfinance, ARCH and pmdarima.
With these tools we will master the most widely used models out there:
- AR (autoregressive model)
- MA (moving-average model)
- ARMA (autoregressive-moving-average model)
- ARIMA (autoregressive integrated moving average model)
- ARIMAX (autoregressive integrated moving average model with exogenous variables)
- SARIA (seasonal autoregressive moving average model)
- SARIMA (seasonal autoregressive integrated moving average model)
- SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables)
- ARCH (autoregressive conditional heteroscedasticity model)
- GARCH (generalized autoregressive conditional heteroscedasticity model)
- VARMA (vector autoregressive moving average model)
We know that time series is one of those topics that always leaves some doubts.
Until now.
This course is exactly what you need to comprehend time series once and for all. Not only that, but you will also get a ton of additional materials – notebooks files, course notes, quiz questions, and many, many exercises – everything is included.
What you’ll learn
- Differentiate between time series data and cross-sectional data.
- Understand the fundamental assumptions of time series data and how to take advantage of them.
- Transforming a data set into a time-series.
- Start coding in Python and learn how to use it for statistical analysis.
- Carry out time-series analysis in Python and interpreting the results, based on the data in question.
- Examine the crucial differences between related series like prices and returns.
- Comprehend the need to normalize data when comparing different time series.
- Encounter special types of time series like White Noise and Random Walks.
- Learn about “autocorrelation” and how to account for it.
- Learn about accounting for “unexpected shocks” via moving averages.
- Discuss model selection in time series and the role residuals play in it.
- Comprehend stationarity and how to test for its existence.
- Acknowledge the notion of integration and understand when, why and how to properly use it.
- Realize the importance of volatility and how we can measure it.
- Forecast the future based on patterns observed in the past.
Table of Contents
Introduction
What does the course cover
Download Additional Resources
Setting Up the Environment
Setting up the environment
Why Python and Jupyter
Installing Anaconda
Jupyter Dashboard
Jupyter Dashboard
Installing the Necessary Packages
Installing Packages Exercise
Installing Packages Exercise Solution
Introduction to Time Series in Python
Introduction to Time Series Data
Notation for Time Series Data
Peculiarities of Time Series Data
Loading the Data
Examining the Data
Plotting the Data
The QQ Plot
Introduction to TimeSeries Data
Notation for Time Series Data
Peculiarities of Time Series Data
Loading the Data
Examining the Data
Plotting the Data
The QQ Plot
Creating a Time Series Object in Python
Transforming String inputs into DateTime Values
Using Dates as an Index
Setting the Frequency
Filling Missing Values
Adding and Removing Columns in a Data Frame
Splitting Up the Data
Transforming String inputs into DateTime Values
Using Date as an Index
Setting the Frequency
Filling Missing Values
Adding and Removing Columns in a Data Frame
Splitting Up the Data
Appendix Updating the Dataset
Working with Time Series in Python
White Noise
Random Walk
Stationarity
Determining Weak Form Stationarity
Seasonality
Correlation Between Past and Present Values
The Autocorrelation Function ACF
The Partial Autocorrelation Function PACF
White Noise
Random Walk
Stationarity
Determining Weak Form Stationarity
Seasonality
Correlation Between Past and Present Values
The Autocorrelation Function ACF
The Partial Autocorrelation Function PACF
Picking the Correct Model
Picking the Correct Model
Picking the Correct Model
Modeling Autoregression The AR Model
The Autoregressive AR Model
Examining the ACF and PACF of Prices
Fitting an AR1 Model for Index Prices
Fitting HigherLag AR Models for Prices
Using Returns Instead of Prices
Examining the ACF and PACF of Returns
Fitting an AR1 Model for Index Returns
Fitting HigherLag AR Models for Returns
Normalizing Values
Model Selection for Normalized Returns
Examining the AR Model Residuals
The Autoregressive AR Model
Examining the ACF and PACF of Prices
Fitting an AR1 Model for Index Prices
Fitting Higher
Using Returns Instead of Prices
Examining the ACF and PACF of Returns
Fitting an AR1 Model for Index Returns
Fitting Higher
Normalizing Values
Model Selection for Normalized Returns AR
Examining the AR Model Residuals
Unexpected Shocks from Past Periods
Adjusting to Shocks The MA Model
The Moving Average MA Model
Fitting an MA1 Model for Returns
Fitting HigherLag MA Models for Returns
Examining the MA Model Residuals for Returns
Model Selection for Normalized Returns MA
Fitting an MA1 Model for Prices
The Moving Average MA Model
Fitting an MA1 Model for Returns
Fitting Higher
Examining the MA Model Residuals for Returns
Model Selection for Normalized Returns MA
Fitting an MA1 Model for Prices
Past Values and Past Errors
Past Values and Past Errors The ARMA Model
The Autoregressive Moving Average ARMA Model
Fitting a Simple ARMA Model for Returns
Fitting a HigherLag ARMA Model for Returns Part 3
Examining the ARMA Model Residuals of Returns
ARMA for Prices
The Autoregressive Moving Average ARMA Model
Fitting a Simple ARMA Model for Returns
Fitting a Higher
Fitting a Higher
Fitting a Higher
Examining the ARMA Model Residuals of Returns
ARMA for Prices
ARMA Models and Non
Modeling NonStationary Data The ARIMA Model
The Autoregressive Integrated Moving Average ARIMA Model
Fitting a Simple ARIMA Model for Prices
Fitting a HigherLag ARIMA Model for Prices Part 2
Higher Levels of Integration
Using ARIMA Models for Returns
Outside Factors and the ARIMAX Model
The Autoregressive Integrated Moving Average ARIMA Model
Fitting a Simple ARIMA Model for Prices
Fitting a HigherLag ARIMA Model for Prices Part 1
Fitting a Higher
Higher Levels of Integration
Using ARIMA Models for Returns
Outside Factors and the ARIMAX Model
Seasonal Models
Predicting Stability
Measuring Volatility The ARCH Model
The ARCH Model
Volatility
A More Detailed Look of the ARCH Model
The archmodel Method
The SImple ARCH Model
The Autoregressive Conditional Heteroscedasticity ARCH Model
Volatility
A More Detailed Look of the ARCH Model
The archmodel Method
The Simple ARCH Model
Higher
An ARMA Equivalent of the ARCH Model
An ARMA Equivalent of the ARCH The GARCH Model
The GARCH Model
The ARMA and the GARCH
The Simple GARCH Model
HigherLAg GARCH Models
The Generalized Autoregressive Conditional Heteroskedasticity GARCH Model
The ARMA and the GARCH
The Simple GARCH Model
Higher
An Alternative to the Model Selection Process
Auto ARIMA
Auto ARIMA
Preparing Python for Model Selection
The Default Best Fit
Basic Auto ARIMA Arguments
Advanced Auto ARIMA Arguments
The Goal Behind Modelling
Forecasting
Introduction to Forecasting
Simple Forecasting Returns with AR and MA
Intermediate MAX Model Forecasting
Advanced Seasonal Forecasting
Auto ARIMA Forecasting
Pitfalls of Forecasting
Forecasting Volatility
Forecasting Appendix Multivariate Forecasting
Business Case
Business Case
Completing 100
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