Python for Time Series Analysis and Forecasting

Python for Time Series Analysis and Forecasting, available at $54.99, has an average rating of 4.15, with 46 lectures, 2 quizzes, based on 414 reviews, and has 2367 subscribers.

You will learn about use Python to perform calculations with time and date based data create models for time series data use models for forecasting identify which models are suitable for a given dataset visualize time series data create ARIMA and exponential smoothing models know how to interpret given models understand time series statistics such as autocorrelation or stationarity use machine learning and deep learning for time series know the alternatives to qualitative methods know how to read a time series plot and understand it (trend, seasonality, constant mean and variance) This course is ideal for individuals who are data analysts working with time series data (which is essentially any data analyst at some point in the career) or people using Python or this course is for people working in various fields like (and not limited to): academia, marketing, business, econometrics, finance, medicine, engineering and science or generally if you have time series data on your table and you do not know what to do with it and Python, take this course! It is particularly useful for data analysts working with time series data (which is essentially any data analyst at some point in the career) or people using Python or this course is for people working in various fields like (and not limited to): academia, marketing, business, econometrics, finance, medicine, engineering and science or generally if you have time series data on your table and you do not know what to do with it and Python, take this course!.

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Summary

Title: Python for Time Series Analysis and Forecasting

Price: $54.99

Average Rating: 4.15

Number of Lectures: 46

Number of Quizzes: 2

Number of Published Lectures: 46

Number of Published Quizzes: 2

Number of Curriculum Items: 48

Number of Published Curriculum Objects: 48

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

use Python to perform calculations with time and date based data
create models for time series data
use models for forecasting
identify which models are suitable for a given dataset
visualize time series data
create ARIMA and exponential smoothing models
know how to interpret given models
understand time series statistics such as autocorrelation or stationarity
use machine learning and deep learning for time series
know the alternatives to qualitative methods
know how to read a time series plot and understand it (trend, seasonality, constant mean and variance)

Who Should Attend

data analysts working with time series data (which is essentially any data analyst at some point in the career)
people using Python
this course is for people working in various fields like (and not limited to): academia, marketing, business, econometrics, finance, medicine, engineering and science
generally if you have time series data on your table and you do not know what to do with it and Python, take this course!

Target Audiences

data analysts working with time series data (which is essentially any data analyst at some point in the career)
people using Python
this course is for people working in various fields like (and not limited to): academia, marketing, business, econometrics, finance, medicine, engineering and science
generally if you have time series data on your table and you do not know what to do with it and Python, take this course!

Use Python to Understand the Now and Predict the Future!

Time series analysis and forecasting is one of the key fields in statistical programming. It allows you to

see patterns in time series data
model this data
finally make forecasts based on those models
and of of this you can now do with the help of Python

Due to modern technology the amount of available data grows substantially from day to day. Successful companies know that. They also know that decisions based on data collected in the past, and modeled for the future, can make a huge difference. Proper understanding and training in time series analysis and forecasting will give you the power to understand and create those models. This can make you an invaluable asset for your company/institution and will boost your career!

What will you learn in this course and how is it structured?

First of all we will discuss the general idea behind time series analysis and forecasting. It is important to know when to use these tools and what they actually do.

After that you will learn about statistical methods used for time series. You will hear about autocorrelation, stationarity and unit root tests. You will also learn how to read a time series chart. This is a crucial skill because things like mean, variance, trend or seasonality are a determining factor for model selection.

We will also create our own time series charts including smoothers and trend lines.

Then you will see how different models work, how they are set up in Python and how you can use them for forecasting and predictive analytics. Models taught are: ARIMA, exponential smoothing, seasonal decomposition and simple models acting as benchmarks. Of course all of this is accompanied by homework assignments.

Where are those methods applied?

In nearly any field you will see those methods applied. Especially econometrics and finance love time series analysis. For example stock data has a time component which makes this sort of data a prime target for forecasting techniques. But of course also in academia, medicine, business or marketing techniques taught in this course are applied.

Is it hard to understand and learn those methods?

Unfortunately learning material on Time Series Analysis Programming in Python is quite technical and needs tons of prior knowledge to be understood.

With this course it is the goal to make modeling and forecasting as intuitive and simple as possible for you.

While you need some knowledge in maths and Python, the course is meant for people without a major in a quantitative field. Basically anybody dealing with time data on a regular basis can benefit from this course.

How do I prepare best to benefit from this course?

It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in Python.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Managing Expectations

Lecture 3: Python for Time Series Analysis

Lecture 4: The Basics of Time Series Analysis and Forecasting

Lecture 5: Select a Forecasting Method

Lecture 6: The Steps of Forecasting – A Guide for Newbies

Lecture 7: Python Script to Download

Chapter 2: Time Series Analysis Background Knowledge

Lecture 1: Time Series Fundamentals

Lecture 2: The Lynx Dataset

Lecture 3: Time Series Vectors and Lags

Lecture 4: Recognizing Time Series Characteristics

Lecture 5: Stationarity

Lecture 6: Autocorrelation

Lecture 7: Visualizing Time Series Data

Lecture 8: Moving Averages and Smoothers

Lecture 9: Homework Assignment #1: US Inflation Rates

Chapter 3: ARIMA for Univariate, Non-Seasonal Data

Lecture 1: Introduction to ARIMA Models in Python

Lecture 2: ARIMA Models for Univariate Time Series

Lecture 3: ARIMA Parameter Selection

Lecture 4: ARIMA Residuals

Lecture 5: Manual ARIMA Model Calculation

Lecture 6: Identify ARIMA Model Parameters: General Rules

Lecture 7: ARIMA Forecasts

Lecture 8: Homework Assignment #2: Singapore LFPR

Chapter 4: Models for Seasonal Data

Lecture 1: The Nottem Dataset

Lecture 2: Seasonal Decomposition

Lecture 3: The STLDecompose Package

Lecture 4: Seasonal Adjustment and Forecasting

Lecture 5: Quantitative Forecasting Methods: An Overview

Lecture 6: Exponential Smoothing Background

Lecture 7: Exponential Smoothing Demo

Lecture 8: What to Do When Numbers Are Not Enough: Qualitative Forecasting Methods

Lecture 9: Introduction to Prophet by Facebook

Lecture 10: Modeling and Forecasting Seasonal Data with Prophet

Lecture 11: Homework Assignment #3: Seasonal Models

Chapter 5: Multivariate Time Series Analysis

Lecture 1: Introduction to Multivariate Time Series Analysis and Dataset Structure

Lecture 2: Our Multivariate Time Series Dataset (and Script)

Lecture 3: Checking for Stationarity and Differencing the MTS

Lecture 4: Vector Autoregressive Models

Lecture 5: Fitting a VAR Model and Identifying the Lag Order

Lecture 6: The Granger Causality Test

Lecture 7: Forecasting with VAR Models

Lecture 8: Further References

Chapter 6: Homework Solutions

Lecture 1: Homework Solution #1: US Inflation Rates

Lecture 2: Homework Solution #2: Singapore LFPR

Lecture 3: Homework Solution #3: Seasonal Models

Instructors

R-Tutorials Training
Data Science Education

Rating Distribution

1 stars: 8 votes
2 stars: 13 votes
3 stars: 54 votes
4 stars: 145 votes
5 stars: 194 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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