• Post category:Udemy (Oct. 2021)
  • Reading time:6 mins read


Learn Python for Pandas, Statsmodels, ARIMA, SARIMAX, Deep Learning, LSTM and Forecasting into Future

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

Description

“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!


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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!

English
language

Content

Introduction
Course Content
Python IDE Installation 1
Python IDE Installation 2
Python IDE Installation 3
Installing Required Libraries
Useful Packages
Source Codes
Notice!!
NumPy 1
NumPy 2
NumPy 3
NumPy 4
NumPy 5
NumPy 6
Pandas 1
Pandas 2
Pandas 3
Pandas 4
Matplotlib 1
Matplotlib 2
Matplotlib 3
Matplotlib 4
Matplotlib 5
Pandas for Time Series Analysis
Create a Datetime with Pandas
Set Datetime as Index
Resampling Method
Pandas Date Frequencies
Statistical Models for Time Series Forecasting
Introduction to ARIMA
ARIMA Model Development 1
ARIMA Model Development 2
ARIMA Model Development 3
Introduction to SARIMAX
SARIMAX Model Development 1
SARIMAX Model Development 2
SARIMAX Model Development 3
Deep Learning for Time Series Forecasting
Introduction to Deep Learning – Basic Concepts
Introduction to Deep Learning – Activation Function
Introduction to Deep Learning – How Neural Network Learn?
Introduction to Deep Learning – Optimization
Introduction to Deep Learning – Recurrent Neural Network
Introduction to Deep Learning – LSTM Method
Development of Univariate LSTM Model 1
Development of Univariate LSTM Model 2
Development of Univariate LSTM Model 3
Development of Univariate LSTM Model 4
Development of Univariate LSTM Model 5
Development of Univariate LSTM Model 6
Development of Multivariate LSTM Model 1
Development of Multivariate LSTM Model 2
Development of Multivariate LSTM Model 3
Development of Multivariate LSTM Model 4
Development of Multivariate LSTM Model 5