Time Series Analysis in Python 2020
4.5 (746 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4,793 students enrolled

Time Series Analysis in Python 2020

Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting
Bestseller
4.5 (746 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4,793 students enrolled
Created by 365 Careers
Last updated 6/2020
English
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 7.5 hours on-demand video
  • 5 articles
  • 18 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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.
Requirements
  • No prior experience with time-series is required.
  • You'll need to install Anaconda. We will show you how to do that step by step.
  • Some general understanding of coding languages is preferred, but not required.
Description

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.

Who this course is for:
  • Aspiring data scientists.
  • Programming beginners.
  • People interested in quantitative finance.
  • Programmers who want to specialize in finance.
  • Finance graduates and professionals who need to better apply their knowledge in Python.
Course content
Expand all 97 lectures 07:21:28
+ Setting Up the Environment
8 lectures 18:40
Setting up the environment - Do not skip, please!
00:56
Why Python and Jupyter?
04:51
Installing Anaconda
03:22
Jupyter Dashboard - Part 1
02:27
Jupyter Dashboard - Part 2
05:14
Installing the Necessary Packages
01:24
Installing Packages - Exercise
00:11
Installing Packages - Exercise Solution
00:14
+ Introduction to Time Series in Python
7 lectures 23:27
Introduction to Time Series Data
3 questions
Notation for Time Series Data
01:26
Notation for Time Series Data
1 question
Peculiarities of Time Series Data
02:42
Peculiarities of Time Series Data
2 questions
Loading the Data
02:06
Loading the Data
1 question
Examining the Data
05:31
Examining the Data
2 questions
Plotting the Data
04:52
Plotting the Data
1 question
The QQ Plot
02:54
The QQ Plot
1 question
+ Creating a Time Series Object in Python
7 lectures 28:47
Transforming String inputs into DateTime Values
1 question
Using Date as an Index
02:49
Using Dates as an Index
1 question
Setting the Frequency
02:56
Setting the Frequency
1 question
Filling Missing Values
06:11
Filling Missing Values
1 question
Adding and Removing Columns in a Data Frame
03:43
Adding and Removing Columns in a Data Frame
1 question
Splitting Up the Data
04:17
Splitting Up the Data
1 question
Appendix: Updating the Dataset
03:57
+ Working with Time Series in Python
8 lectures 38:42
White Noise
06:54
White Noise
2 questions
Random Walk
05:31
Random Walk
1 question
Stationarity
02:30
Stationarity
1 question
Determining Weak Form Stationarity
05:49
Determining Weak Form Stationarity
1 question
Seasonality
05:12
Seasonality
1 question
Correlation Between Past and Present Values
01:32
Correlation Between Past and Present Values
1 question
The Autocorrelation Function (ACF)
06:00
The Autocorrelation Function (ACF)
1 question
The Partial Autocorrelation Function (PACF)
05:14
The Partial Autocorrelation Function (PACF)
1 question
+ Picking the Correct Model
1 lecture 02:32
Picking the Correct Model
02:32
Picking the Correct Model
1 question
+ Modeling Autoregression: The AR Model
12 lectures 53:57
The Autoregressive (AR) Model
1 question
Examining the ACF and PACF of Prices
04:58
Examining the ACF and PACF of Prices
1 question
Fitting an AR(1) Model for Index Prices
04:54
Fitting an AR(1) Model for Index Prices
1 question
Fitting Higher-Lag AR Models for Prices
09:16
Fitting Higher-Lag AR Models for Prices
1 question
Using Returns Instead of Prices
05:41
Using Returns Instead of Prices
1 question
Examining the ACF and PACF of Returns
02:07
Examining the ACF and PACF of Returns
1 question
Fitting an AR(1) Model for Index Returns
02:33
Fitting an AR(1) Model for Index Returns
1 question
Fitting Higher-Lag AR Models for Returns
03:45
Fitting Higher-Lag AR Models for Returns
1 question
Normalizing Values
05:23
Normalizing Values
1 question
Model Selection for Normalized Returns (AR)
02:37
Model Selection for Normalized Returns
1 question
Examining the AR Model Residuals
05:52
Examining the AR Model Residuals
1 question
Unexpected Shocks from Past Periods
01:23
+ Adjusting to Shocks: The MA Model
7 lectures 34:06
The Moving Average (MA) Model
05:04
The Moving Average (MA) Model
1 question
Fitting an MA(1) Model for Returns
03:49
Fitting an MA(1) Model for Returns
1 question
Fitting Higher-Lag MA Models for Returns
07:30
Fitting Higher-Lag MA Models for Returns
1 question
Examining the MA Model Residuals for Returns
06:19
Examining the MA Model Residuals for Returns
1 question
Model Selection for Normalized Returns (MA)
03:39
Model Selection for Normalized Returns (MA)
1 question
Fitting an MA(1) Model for Prices
05:20
Fitting an MA(1) Model for Prices
1 question
Past Values and Past Errors
02:25
+ Past Values and Past Errors: The ARMA Model
8 lectures 41:52
The Autoregressive Moving Average (ARMA) Model
03:34
The Autoregressive Moving Average (ARMA) Model
1 question
Fitting a Simple ARMA Model for Returns
04:18
Fitting a Simple ARMA Model for Returns
1 question
Fitting a Higher-Lag ARMA Model for Returns - Part 1
05:15
Fitting a Higher-Lag ARMA Model for Returns - Part 2
05:15
Fitting a Higher-Lag ARMA Model for Returns - Part 3
06:20
Fitting a Higher-Lag ARMA Model for Returns - Part 3
1 question
Examining the ARMA Model Residuals of Returns
07:15
Examining the ARMA Model Residuals of Returns
1 question
ARMA for Prices
07:57
ARMA for Prices
1 question
ARMA Models and Non-Stationary Data
01:58
+ Modeling Non-Stationary Data: The ARIMA Model
9 lectures 45:30
The Autoregressive Integrated Moving Average (ARIMA) Model
06:24
The Autoregressive Integrated Moving Average (ARIMA) Model
1 question
Fitting a Simple ARIMA Model for Prices
05:46
Fitting a Simple ARIMA Model for Prices
1 question
Fitting a Higher-Lag ARIMA Model for Prices - Part 2
06:13
Fitting a Higher-Lag ARIMA Model for Prices - Part 2
1 question
Higher Levels of Integration
03:57
Higher Levels of Integration
1 question
Using ARIMA Models for Returns
03:21
Using ARIMA Models for Returns
1 question
Outside Factors and the ARIMAX Model
04:09
Outside Factors and the ARIMAX Model
1 question
Seasonal Models - SARIMAX
07:48
Predicting Stability
01:41