Time Series Analysis In Python 2023

Time Series Analysis In Python 2023
Last updated 12/2020
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

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.

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

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 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)

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 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 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

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.