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2021-01-05 11:02:48
30-Day Money-Back Guarantee
Business Business Analytics & Intelligence Time Series Analysis

Time Series Analysis in Python 2021

Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting
Bestseller
Rating: 4.5 out of 54.5 (1,091 ratings)
6,917 students
Created by 365 Careers
Last updated 12/2020
English
English [Auto]
30-Day Money-Back Guarantee

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.
Curated for the Udemy for Business collection

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.

Featured review

Naveen Kalhan
Naveen Kalhan
31 courses
9 reviews
Rating: 5.0 out of 58 months ago
Absolutely learned a lot during this course... most importantly, thank you for opening up further gateways to go beyond with this set of knowledge. this course really made it easier for me. Thank you for your good and hard work 365 career. REALLY LIKE THIS COURSE

Course content

15 sections • 97 lectures • 7h 21m total length

  • Preview04:54
  • Download Additional Resources
    00:17

  • 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

  • Preview03:56
  • 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

  • Preview04:54
  • 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

  • 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
    02:32
  • Picking the Correct Model
    1 question

  • Preview05:28
  • 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

  • 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

  • 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

  • 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
  • Preview06:11
  • 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

Instructor

365 Careers
Creating opportunities for Business & Finance students
365 Careers
  • 4.5 Instructor Rating
  • 390,241 Reviews
  • 1,336,407 Students
  • 70 Courses

365 Careers is the #1 best-selling provider of finance courses on Udemy. The company’s courses have been taken by more than 1,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings.  

Currently, the firm focuses on the following topics on Udemy:  

1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA

2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics

3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing

4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook

5) Blockchain for Business

All of the company’s courses are:  

Pre-scripted  

Hands-on  

Laser-focused  

Engaging  

Real-life tested  

By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.  

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers’ courses are the perfect place to start. 

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