Last updated 12/2020
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.78 GB | Duration: 7h 21m
Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting
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 resourcesBut 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 jobWe 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.
Overview
Section 1: Introduction
Lecture 1 What does the course cover?
Lecture 2 Download Additional Resources
Section 2: Setting Up the Environment
Lecture 3 Setting up the environment – Do not skip, please!
Lecture 4 Why Python and Jupyter?
Lecture 5 Installing Anaconda
Lecture 6 Jupyter Dashboard – Part 1
Lecture 7 Jupyter Dashboard – Part 2
Lecture 8 Installing the Necessary Packages
Lecture 9 Installing Packages – Exercise
Lecture 10 Installing Packages – Exercise Solution
Section 3: Introduction to Time Series in Python
Lecture 11 Introduction to Time-Series Data
Lecture 12 Notation for Time Series Data
Lecture 13 Peculiarities of Time Series Data
Lecture 14 Loading the Data
Lecture 15 Examining the Data
Lecture 16 Plotting the Data
Lecture 17 The QQ Plot
Section 4: Creating a Time Series Object in Python
Lecture 18 Transforming String inputs into DateTime Values
Lecture 19 Using Date as an Index
Lecture 20 Setting the Frequency
Lecture 21 Filling Missing Values
Lecture 22 Adding and Removing Columns in a Data Frame
Lecture 23 Splitting Up the Data
Lecture 24 Appendix: Updating the Dataset
Section 5: Working with Time Series in Python
Lecture 25 White Noise
Lecture 26 Random Walk
Lecture 27 Stationarity
Lecture 28 Determining Weak Form Stationarity
Lecture 29 Seasonality
Lecture 30 Correlation Between Past and Present Values
Lecture 31 The Autocorrelation Function (ACF)
Lecture 32 The Partial Autocorrelation Function (PACF)
Section 6: Picking the Correct Model
Lecture 33 Picking the Correct Model
Section 7: Modeling Autoregression: The AR Model
Lecture 34 The Autoregressive (AR) Model
Lecture 35 Examining the ACF and PACF of Prices
Lecture 36 Fitting an AR(1) Model for Index Prices
Lecture 37 Fitting Higher-Lag AR Models for Prices
Lecture 38 Using Returns Instead of Prices
Lecture 39 Examining the ACF and PACF of Returns
Lecture 40 Fitting an AR(1) Model for Index Returns
Lecture 41 Fitting Higher-Lag AR Models for Returns
Lecture 42 Normalizing Values
Lecture 43 Model Selection for Normalized Returns (AR)
Lecture 44 Examining the AR Model Residuals
Lecture 45 Unexpected Shocks from Past Periods
Section 8: Adjusting to Shocks: The MA Model
Lecture 46 The Moving Average (MA) Model
Lecture 47 Fitting an MA(1) Model for Returns
Lecture 48 Fitting Higher-Lag MA Models for Returns
Lecture 49 Examining the MA Model Residuals for Returns
Lecture 50 Model Selection for Normalized Returns (MA)
Lecture 51 Fitting an MA(1) Model for Prices
Lecture 52 Past Values and Past Errors
Section 9: Past Values and Past Errors: The ARMA Model
Lecture 53 The Autoregressive Moving Average (ARMA) Model
Lecture 54 Fitting a Simple ARMA Model for Returns
Lecture 55 Fitting a Higher-Lag ARMA Model for Returns – Part 1
Lecture 56 Fitting a Higher-Lag ARMA Model for Returns – Part 2
Lecture 57 Fitting a Higher-Lag ARMA Model for Returns – Part 3
Lecture 58 Examining the ARMA Model Residuals of Returns
Lecture 59 ARMA for Prices
Lecture 60 ARMA Models and Non-Stationary Data
Section 10: Modeling Non-Stationary Data: The ARIMA Model
Lecture 61 The Autoregressive Integrated Moving Average (ARIMA) Model
Lecture 62 Fitting a Simple ARIMA Model for Prices
Lecture 63 Fitting a Higher-Lag ARIMA Model for Prices – Part 1
Lecture 64 Fitting a Higher-Lag ARIMA Model for Prices – Part 2
Lecture 65 Higher Levels of Integration
Lecture 66 Using ARIMA Models for Returns
Lecture 67 Outside Factors and the ARIMAX Model
Lecture 68 Seasonal Models – SARIMAX
Lecture 69 Predicting Stability
Section 11: Measuring Volatility: The ARCH Model
Lecture 70 The Autoregressive Conditional Heteroscedasticity (ARCH) Model
Lecture 71 Volatility
Lecture 72 A More Detailed Look of the ARCH Model
Lecture 73 The arch_model Method
Lecture 74 The Simple ARCH Model
Lecture 75 Higher-Lag ARCH Models
Lecture 76 An ARMA Equivalent of the ARCH Model
Section 12: An ARMA Equivalent of the ARCH: The GARCH Model
Lecture 77 The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model
Lecture 78 The ARMA and the GARCH
Lecture 79 The Simple GARCH Model
Lecture 80 Higher-Lag GARCH Models
Lecture 81 An Alternative to the Model Selection Process
Section 13: Auto ARIMA
Lecture 82 Auto ARIMA
Lecture 83 Preparing Python for Model Selection
Lecture 84 The Default Best Fit
Lecture 85 Basic Auto ARIMA Arguments
Lecture 86 Advanced Auto ARIMA Arguments
Lecture 87 The Goal Behind Modelling
Section 14: Forecasting
Lecture 88 Introduction to Forecasting
Lecture 89 Simple Forecasting Returns with AR and MA
Lecture 90 Intermediate ("MAX" Model) Forecasting
Lecture 91 Advanced (Seasonal) Forecasting
Lecture 92 Auto ARIMA Forecasting
Lecture 93 Pitfalls of Forecasting
Lecture 94 Forecasting Volatility
Lecture 95 Forecasting Appendix: Multivariate Forecasting
Section 15: Business Case
Lecture 96 Business Case – A Look Into the Automobile Industry
Lecture 97 Completing 100%
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.
Homepage
https://www.udemy.com/course/time-series-analysis-in-python/
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