Time Series Analysis in Python

About Course

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 point

· 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 get?

· Active Q&A support

· Supplementary materials – notebook files, course notes, quiz questions, exercises

· All the knowledge to get a job with time series analysis

· A community of data science enthusiasts

· A certificate of completion

· Access to future updates

· Solve real-life business cases that will get you the job

We are happy to offer a 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.

Why wait? Every day is a missed opportunity.

Click the “Buy Now” button and start mastering time series in Python today.

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.

Course Content

01 – Introduction

001 – What does the course cover.mp4

00:00
002 – Download Additional Resources.html

00:00

02 – Setting Up the Environment

03 – Introduction to Time Series in Python

001 – Introduction to Time Series Data.html

00:00
002 – Notation for Time Series Data.html

00:00
003 – Peculiarities of Time Series Data.html

00:00
004 – Loading the Data.html

00:00
005 – Examining the Data.html

00:00
006 – Plotting the Data.html

00:00
007 – The QQ Plot.html

00:00
011 – Introduction to TimeSeries Data.mp4

00:00
012 – Notation for Time Series Data.mp4

00:00
013 – Peculiarities of Time Series Data.mp4

00:00
014 – Loading the Data.mp4

00:00
015 – Examining the Data.mp4

00:00
016 – Plotting the Data.mp4

00:00
017 – The QQ Plot.mp4

00:00

04 – Creating a Time Series Object in Python

008 – Transforming String inputs into DateTime Values.html

00:00
009 – Using Dates as an Index.html

00:00
010 – Setting the Frequency.html

00:00
011 – Filling Missing Values.html

00:00
012 – Adding and Removing Columns in a Data Frame.html

00:00
013 – Splitting Up the Data.html

00:00
018 – Transforming String inputs into DateTime Values.mp4

00:00
019 – Using Date as an Index.mp4

00:00
020 – Setting the Frequency.mp4

00:00
021 – Filling Missing Values.mp4

00:00
022 – Adding and Removing Columns in a Data Frame.mp4

00:00
023 – Splitting Up the Data.mp4

00:00
024 – Appendix Updating the Dataset.html

00:00

05 – Working with Time Series in Python

014 – White Noise.html

00:00
015 – Random Walk.html

00:00
016 – Stationarity.html

00:00
017 – Determining Weak Form Stationarity.html

00:00
018 – Seasonality.html

00:00
019 – Correlation Between Past and Present Values.html

00:00
020 – The Autocorrelation Function ACF.html

00:00
021 – The Partial Autocorrelation Function PACF.html

00:00
025 – White Noise.mp4

00:00
026 – Random Walk.mp4

00:00
027 – Stationarity.mp4

00:00
028 – Determining Weak Form Stationarity.mp4

00:00
029 – Seasonality.mp4

00:00
030 – Correlation Between Past and Present Values.mp4

00:00
031 – The Autocorrelation Function ACF.mp4

00:00
032 – The Partial Autocorrelation Function PACF.mp4

00:00

06 – Picking the Correct Model

07 – Modeling Autoregression The AR Model

023 – The Autoregressive AR Model.html

00:00
024 – Examining the ACF and PACF of Prices.html

00:00
025 – Fitting an AR1 Model for Index Prices.html

00:00
026 – Fitting HigherLag AR Models for Prices.html

00:00
027 – Using Returns Instead of Prices.html

00:00
028 – Examining the ACF and PACF of Returns.html

00:00
029 – Fitting an AR1 Model for Index Returns.html

00:00
030 – Fitting HigherLag AR Models for Returns.html

00:00
031 – Normalizing Values.html

00:00
032 – Model Selection for Normalized Returns.html

00:00
033 – Examining the AR Model Residuals.html

00:00
034 – The Autoregressive AR Model.mp4

00:00
035 – Examining the ACF and PACF of Prices.mp4

00:00
036 – Fitting an AR1 Model for Index Prices.mp4

00:00
037 – Fitting Higher.mp4

00:00
038 – Using Returns Instead of Prices.mp4

00:00
039 – Examining the ACF and PACF of Returns.mp4

00:00
040 – Fitting an AR1 Model for Index Returns.mp4

00:00
041 – Fitting Higher.mp4

00:00
042 – Normalizing Values.mp4

00:00
043 – Model Selection for Normalized Returns AR.mp4

00:00
044 – Examining the AR Model Residuals.mp4

00:00
045 – Unexpected Shocks from Past Periods.mp4

00:00

08 – Adjusting to Shocks The MA Model

034 – The Moving Average MA Model.html

00:00
035 – Fitting an MA1 Model for Returns.html

00:00
036 – Fitting HigherLag MA Models for Returns.html

00:00
037 – Examining the MA Model Residuals for Returns.html

00:00
038 – Model Selection for Normalized Returns MA.html

00:00
039 – Fitting an MA1 Model for Prices.html

00:00
046 – The Moving Average MA Model.mp4

00:00
047 – Fitting an MA1 Model for Returns.mp4

00:00
048 – Fitting Higher.mp4

00:00
049 – Examining the MA Model Residuals for Returns.mp4

00:00
050 – Model Selection for Normalized Returns MA.mp4

00:00
051 – Fitting an MA1 Model for Prices.mp4

00:00
052 – Past Values and Past Errors.mp4

00:00

09 – Past Values and Past Errors The ARMA Model

040 – The Autoregressive Moving Average ARMA Model.html

00:00
041 – Fitting a Simple ARMA Model for Returns.html

00:00
042 – Fitting a HigherLag ARMA Model for Returns Part 3.html

00:00
043 – Examining the ARMA Model Residuals of Returns.html

00:00
044 – ARMA for Prices.html

00:00
053 – The Autoregressive Moving Average ARMA Model.mp4

00:00
054 – Fitting a Simple ARMA Model for Returns.mp4

00:00
055 – Fitting a Higher.mp4

00:00
056 – Fitting a Higher.mp4

00:00
057 – Fitting a Higher.mp4

00:00
058 – Examining the ARMA Model Residuals of Returns.mp4

00:00
059 – ARMA for Prices.mp4

00:00
060 – ARMA Models and Non.mp4

00:00

10 – Modeling NonStationary Data The ARIMA Model

045 – The Autoregressive Integrated Moving Average ARIMA Model.html

00:00
046 – Fitting a Simple ARIMA Model for Prices.html

00:00
047 – Fitting a HigherLag ARIMA Model for Prices Part 2.html

00:00
048 – Higher Levels of Integration.html

00:00
049 – Using ARIMA Models for Returns.html

00:00
050 – Outside Factors and the ARIMAX Model.html

00:00
061 – The Autoregressive Integrated Moving Average ARIMA Model.mp4

00:00
062 – Fitting a Simple ARIMA Model for Prices.mp4

00:00
063 – Fitting a HigherLag ARIMA Model for Prices Part 1.mp4

00:00
064 – Fitting a Higher.mp4

00:00
065 – Higher Levels of Integration.mp4

00:00
066 – Using ARIMA Models for Returns.mp4

00:00
067 – Outside Factors and the ARIMAX Model.mp4

00:00
068 – Seasonal Models.mp4

00:00
069 – Predicting Stability.mp4

00:00

11 – Measuring Volatility The ARCH Model

051 – The ARCH Model.html

00:00
052 – Volatility.html

00:00
053 – A More Detailed Look of the ARCH Model.html

00:00
054 – The archmodel Method.html

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055 – The SImple ARCH Model.html

00:00
070 – The Autoregressive Conditional Heteroscedasticity ARCH Model.mp4

00:00
071 – Volatility.mp4

00:00
072 – A More Detailed Look of the ARCH Model.mp4

00:00
073 – The archmodel Method.mp4

00:00
074 – The Simple ARCH Model.mp4

00:00
075 – Higher.mp4

00:00
076 – An ARMA Equivalent of the ARCH Model.mp4

00:00

12 – An ARMA Equivalent of the ARCH The GARCH Model

056 – The GARCH Model.html

00:00
057 – The ARMA and the GARCH.html

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058 – The Simple GARCH Model.html

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059 – HigherLAg GARCH Models.html

00:00
077 – The Generalized Autoregressive Conditional Heteroskedasticity GARCH Model.mp4

00:00
078 – The ARMA and the GARCH.mp4

00:00
079 – The Simple GARCH Model.mp4

00:00
080 – Higher.mp4

00:00
081 – An Alternative to the Model Selection Process.mp4

00:00

13 – Auto ARIMA

14 – Forecasting

15 – Business Case

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About Course

What Will You Learn?

Course Content

01 – Introduction

001 – What does the course cover.mp4

002 – Download Additional Resources.html

02 – Setting Up the Environment

003 – Setting up the environment.mp4

004 – Why Python and Jupyter.mp4

005 – Installing Anaconda.mp4

006 – Jupyter Dashboard.mp4

007 – Jupyter Dashboard.mp4

008 – Installing the Necessary Packages.mp4

009 – Installing Packages Exercise.html

010 – Installing Packages Exercise Solution.html

03 – Introduction to Time Series in Python

001 – Introduction to Time Series Data.html

002 – Notation for Time Series Data.html

003 – Peculiarities of Time Series Data.html

004 – Loading the Data.html

005 – Examining the Data.html

006 – Plotting the Data.html

007 – The QQ Plot.html

011 – Introduction to TimeSeries Data.mp4

012 – Notation for Time Series Data.mp4

013 – Peculiarities of Time Series Data.mp4

014 – Loading the Data.mp4

015 – Examining the Data.mp4

016 – Plotting the Data.mp4

017 – The QQ Plot.mp4

04 – Creating a Time Series Object in Python

008 – Transforming String inputs into DateTime Values.html

009 – Using Dates as an Index.html

010 – Setting the Frequency.html

011 – Filling Missing Values.html

012 – Adding and Removing Columns in a Data Frame.html

013 – Splitting Up the Data.html

018 – Transforming String inputs into DateTime Values.mp4

019 – Using Date as an Index.mp4

020 – Setting the Frequency.mp4

021 – Filling Missing Values.mp4

022 – Adding and Removing Columns in a Data Frame.mp4

023 – Splitting Up the Data.mp4

024 – Appendix Updating the Dataset.html

05 – Working with Time Series in Python

014 – White Noise.html

015 – Random Walk.html

016 – Stationarity.html

017 – Determining Weak Form Stationarity.html

018 – Seasonality.html

019 – Correlation Between Past and Present Values.html

020 – The Autocorrelation Function ACF.html

021 – The Partial Autocorrelation Function PACF.html

025 – White Noise.mp4

026 – Random Walk.mp4

027 – Stationarity.mp4

028 – Determining Weak Form Stationarity.mp4

029 – Seasonality.mp4

030 – Correlation Between Past and Present Values.mp4

031 – The Autocorrelation Function ACF.mp4

032 – The Partial Autocorrelation Function PACF.mp4

06 – Picking the Correct Model

022 – Picking the Correct Model.html

033 – Picking the Correct Model.mp4

07 – Modeling Autoregression The AR Model

023 – The Autoregressive AR Model.html

024 – Examining the ACF and PACF of Prices.html

025 – Fitting an AR1 Model for Index Prices.html

026 – Fitting HigherLag AR Models for Prices.html

027 – Using Returns Instead of Prices.html

028 – Examining the ACF and PACF of Returns.html

029 – Fitting an AR1 Model for Index Returns.html

030 – Fitting HigherLag AR Models for Returns.html

031 – Normalizing Values.html

032 – Model Selection for Normalized Returns.html

033 – Examining the AR Model Residuals.html

034 – The Autoregressive AR Model.mp4

035 – Examining the ACF and PACF of Prices.mp4

036 – Fitting an AR1 Model for Index Prices.mp4

037 – Fitting Higher.mp4