Time Series Analysis and Forecasting with Python

Time Series Analysis and Forecasting with Python, available at $74.99, has an average rating of 4.29, with 52 lectures, based on 454 reviews, and has 5760 subscribers.

You will learn about Basic Packages, NumPy, Pandas & Matplotlib Time Series with Pandas (Creating Date Time index, Resampling, …) Analyzing Time Series Data Using Statsmodels Package The Concept of ARIMA and SARIMAX method and How to Forecast into the Future Using Them The Concept of Deep Learning from A-Z Forecast into the Future Using LSTM Model for Single Variant Forecast into the Future Using LSTM Model for Multi Variant This course is ideal for individuals who are Data Science Enthusiast or Beginner Programmers or Python Developers or Recheachers who like to forecast into future or Data Analysts or Anyone who is interested in Time Series and Future Forecasting It is particularly useful for Data Science Enthusiast or Beginner Programmers or Python Developers or Recheachers who like to forecast into future or Data Analysts or Anyone who is interested in Time Series and Future Forecasting.

Enroll now: Time Series Analysis and Forecasting with Python

Summary

Title: Time Series Analysis and Forecasting with Python

Price: $74.99

Average Rating: 4.29

Number of Lectures: 52

Number of Published Lectures: 52

Number of Curriculum Items: 52

Number of Published Curriculum Objects: 52

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Basic Packages, NumPy, Pandas & Matplotlib
Time Series with Pandas (Creating Date Time index, Resampling, …)
Analyzing Time Series Data Using Statsmodels Package
The Concept of ARIMA and SARIMAX method and How to Forecast into the Future Using Them
The Concept of Deep Learning from A-Z
Forecast into the Future Using LSTM Model for Single Variant
Forecast into the Future Using LSTM Model for Multi Variant

Who Should Attend

Data Science Enthusiast
Beginner Programmers
Python Developers
Recheachers who like to forecast into future
Data Analysts
Anyone who is interested in Time Series and Future Forecasting

Target Audiences

Data Science Enthusiast
Beginner Programmers
Python Developers
Recheachers who like to forecast into future
Data Analysts
Anyone who is interested in Time Series and Future Forecasting

“Time Series Analysis and Forecasting with Python” Course is an ultimate source for learning the concepts of Time Series and forecast into the future.

In this course, the most famous methods such as statistical methods (ARIMA and SARIMAX) and Deep Learning Method (LSTM) are explained in detail. Furthermore, several Real World projects are developed in a Python environment and have been explained line by line!

If you are a researcher, a student, a programmer, or a data science enthusiast that is seeking a course that shows you all about time series and prediction from A-Z, you are in a right place. Just check out what you will learn in this course below:

Basic libraries (NumPy, Pandas, Matplotlib)
How to use Pandas library to create DateTime index and how to set that as your Dataset index
What are statistical models?
How to forecast into future using the ARIMA model?
How to capture the seasonality using the SARIMAX model?
How to use endogenous variables and predict into future?
What is Deep Learning (Very Basic Concepts)
All about Artificial and Recurrent Neural Network!
How the LSTM method Works!
How to develop an LSTM model with a single variate?
How to develop an LSTM model using multiple variables (Multivariate)

As I mentioned above, in this course we tried to explain how you can develop an LSTM model when you have several predictors (variables) for the first time and you can use that for several applications and use the source code for your project as well!

This course is for Everyone! yes everyone! that wants t to learn time-series and forecasting into the future using statistics and artificial intelligence with any kind of background! Even if you are not a programmer, I show you how to code and develop your model line by line!

If you want to master the basics of Machine Learning in Python as well, you can check my other courses!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Content

Lecture 2: Python IDE Installation 1

Lecture 3: Python IDE Installation 2

Lecture 4: Python IDE Installation 3

Lecture 5: Installing Required Libraries

Chapter 2: Useful Packages

Lecture 1: Source Codes

Lecture 2: Notice!!

Lecture 3: NumPy 1

Lecture 4: NumPy 2

Lecture 5: NumPy 3

Lecture 6: NumPy 4

Lecture 7: NumPy 5

Lecture 8: NumPy 6

Lecture 9: Pandas 1

Lecture 10: Pandas 2

Lecture 11: Pandas 3

Lecture 12: Pandas 4

Lecture 13: Matplotlib 1

Lecture 14: Matplotlib 2

Lecture 15: Matplotlib 3

Lecture 16: Matplotlib 4

Lecture 17: Matplotlib 5

Chapter 3: Pandas for Time Series Analysis

Lecture 1: Create a Datetime with Pandas

Lecture 2: Set Datetime as Index

Lecture 3: Resampling Method

Lecture 4: Pandas Date Frequencies

Chapter 4: Statistical Models for Time Series Forecasting

Lecture 1: Introduction to ARIMA

Lecture 2: ARIMA Model Development 1

Lecture 3: ARIMA Model Development 2

Lecture 4: ARIMA Model Development 3

Lecture 5: Introduction to SARIMAX

Lecture 6: SARIMAX Model Development 1

Lecture 7: SARIMAX Model Development 2

Lecture 8: SARIMAX Model Development 3

Chapter 5: Deep Learning for Time Series Forecasting

Lecture 1: Introduction to Deep Learning – Basic Concepts

Lecture 2: Introduction to Deep Learning – Activation Function

Lecture 3: Introduction to Deep Learning – How Neural Network Learn?

Lecture 4: Introduction to Deep Learning – Optimization

Lecture 5: Introduction to Deep Learning – Recurrent Neural Network

Lecture 6: Introduction to Deep Learning – LSTM Method

Lecture 7: Development of Univariate LSTM Model 1

Lecture 8: Development of Univariate LSTM Model 2

Lecture 9: Development of Univariate LSTM Model 3

Lecture 10: Development of Univariate LSTM Model 4

Lecture 11: Development of Univariate LSTM Model 5

Lecture 12: Development of Univariate LSTM Model 6

Lecture 13: Development of Multivariate LSTM Model 1

Lecture 14: Development of Multivariate LSTM Model 2

Lecture 15: Development of Multivariate LSTM Model 3

Lecture 16: Development of Multivariate LSTM Model 4

Lecture 17: Development of Multivariate LSTM Model 5

Chapter 6: Bonus

Lecture 1: Bonus Lecture

Instructors

Navid Shirzadi, Ph.D.
Data Science & Optimization Expert

Rating Distribution

1 stars: 10 votes
2 stars: 10 votes
3 stars: 38 votes
4 stars: 126 votes
5 stars: 270 votes

Frequently Asked Questions

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