HOT

Mastering Time Series Forecasting with Python

Development

Rating3.9
Enroll Now

Mastering Time Series Forecasting with Python, available at $54.99, has an average rating of 3.9, with 119 lectures, based on 137 reviews, and has 22228 subscribers.

You will learn about Python Programing Basic to Advanced Time Series Methods Time Series Visualization in Python Auto Regressive Methods, Moving Average, Exponential Moving Average Linear Regression and Evaluation Additive and Multiplicative Models ARMA, ARIMA, SARIMA in Python ACF and PACF Auto ARIMA in Python Stationary and Non Stationary GARCH Models This course is ideal for individuals who are Anyone who are interested to do time series analysis and forecasting or Want to do advanced real time forecasting It is particularly useful for Anyone who are interested to do time series analysis and forecasting or Want to do advanced real time forecasting.

Enroll now: Mastering Time Series Forecasting with Python

Summary

Title: Mastering Time Series Forecasting with Python

Price: $54.99

Average Rating: 3.9

Number of Lectures: 119

Number of Published Lectures: 119

Number of Curriculum Items: 119

Number of Published Curriculum Objects: 119

Original Price: $174.99

Quality Status: approved

Status: Live

What You Will Learn

  • Python Programing
  • Basic to Advanced Time Series Methods
  • Time Series Visualization in Python
  • Auto Regressive Methods,
  • Moving Average, Exponential Moving Average
  • Linear Regression and Evaluation
  • Additive and Multiplicative Models
  • ARMA, ARIMA, SARIMA in Python
  • ACF and PACF
  • Auto ARIMA in Python
  • Stationary and Non Stationary
  • GARCH Models

Who Should Attend

  • Anyone who are interested to do time series analysis and forecasting
  • Want to do advanced real time forecasting

Target Audiences

  • Anyone who are interested to do time series analysis and forecasting
  • Want to do advanced real time forecasting

Welcome to Mastering Time Series Forecasting in Python

Time series analysis and forecasting is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. Many industries looking for a Data Scientist with these skills. This course covers all types of modeling techniques for forecasting and analysis.

We start with programming in Python which is the essential skill required and then we will exploring the fundamental time series theory to help you understand the modeling that comes afterward.

Then throughout the course, we will work with a number of Python libraries, providing you with complete training. We will use the powerful time-series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, statsmodels, Sklearn, and ARCH.

With these tools we will master the most widely used models out there:

  • Additive Model

  • Multiplicative Model

  • AR (autoregressive model)

  • Simple Moving Average

  • Weighted Moving Average

  • Exponential Moving Average

  • ARMA (autoregressive-moving-average model)

  • ARIMA (autoregressive integrated moving average model)

  • Auto ARIMA

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 the time series once and for all. Not only that, but you will also get a ton of additional materials – notebooks files, course notes – everything is included.

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is Time Series Data

Lecture 2: Time Series Components

Lecture 3: Download the Resources

Lecture 4: Good Learning Practice

Chapter 2: Setting Google Colab

Lecture 1: Install Google Colab to your mail id

Lecture 2: Integrate Google Drive to Colab to Load Data

Chapter 3: Time Series Visualizations

Lecture 1: Download the Resources

Lecture 2: Types of Charts for Time Series

Lecture 3: Setting up Google Colab

Lecture 4: Load the Data

Lecture 5: Line Chart

Lecture 6: Hue the Line Chart

Lecture 7: Area Chart

Lecture 8: Bar Plot

Lecture 9: Proposition and Stacked Bar, Area Chart

Lecture 10: Heatmaps

Chapter 4: Linear Regression Intution

Lecture 1: Download the Resources

Lecture 2: Intuition of Linear Regression

Lecture 3: Exploratory Data Analysis

Lecture 4: EDA – Quantitative Technique

Lecture 5: EDA – Graphical Technique

Lecture 6: Simple Linear Regression – Python

Lecture 7: Simple Linear Regression – Sklearn (Python)

Lecture 8: Simple Linear Regression – Statsmodels (Python)

Lecture 9: Model Evaluation – R^2, ANOVA

Lecture 10: Model Evaluation – Python

Chapter 5: Regression for Time Series Forecasting

Lecture 1: Regression with Time

Lecture 2: Download the Resources

Lecture 3: Data Preprocessing in Python

Lecture 4: Splitting Data into Training and Testing Sets in Python

Lecture 5: Train Regression Model with Time in Python

Lecture 6: Forecasting with Confidence Interval and Visualizations in Python

Chapter 6: Additive Time Series Model with Statsmodels

Lecture 1: Additive Model

Lecture 2: Data Analysis in Python

Lecture 3: Creating Seasonal Features

Lecture 4: Splitting Data into Training and Testing Sets

Lecture 5: Training Additive Model in Statsmodels

Lecture 6: Additive Model Forecasting and Visualizations

Chapter 7: Multiplicative Time Series Model

Lecture 1: Multiplicative Model

Lecture 2: Step-1: Trend Model

Lecture 3: Step-2: Calculate Seasonal Deviation

Lecture 4: Step-3: Seasonal Corrector Factor

Lecture 5: Fitted values and Forecasting with Multiplicative Model

Lecture 6: Margin of Error and Confidence Interval

Lecture 7: Visualizing Forecasted Data

Chapter 8: Auto Regressive Methods

Lecture 1: Auto Regressive Methods

Lecture 2: Download the Resources

Lecture 3: Setting Up for Model Building

Lecture 4: Data Preprocessing

Lecture 5: ACF & PACF

Lecture 6: Making Data Stationary

Lecture 7: Training AR Model

Lecture 8: Fitted and Forecasting values with AR Model

Lecture 9: AR Model Evaluation

Chapter 9: Smoothing Methods (Moving Average)

Lecture 1: Smoothing Techniques

Lecture 2: Download the Resources

Lecture 3: Naive Forecasting Model

Lecture 4: Naive Forecasting Model in Python – part 1

Lecture 5: Naive Forecasting Model in Python – part 2

Lecture 6: Simple Moving Average

Lecture 7: Simple Moving Average in Python

Lecture 8: Simple Moving Average order (q) in Python

Lecture 9: Weighted Moving Average

Lecture 10: Weighted Moving Average in Python

Lecture 11: Exponential Moving Average

Lecture 12: Exponential Moving Average in Python

Chapter 10: Non Seasonal ARIMA models

Lecture 1: ARMA

Lecture 2: Non Seasonal ARIMA

Lecture 3: Downloads Data and Notebook

Lecture 4: ARMA – Load Data

Lecture 5: ARMA – Split the Data into train and test sets

Lecture 6: ARMA – Steps to Build the Models

Lecture 7: ARMA – Augmented Dickey Fuller test for stationary

Lecture 8: ARMA – Converting Data into Stationary

Lecture 9: ARMA – ACF & PACF , Train ARMA(p,q)

Lecture 10: ARMA – Evaluation

Lecture 11: ARMA – Visualizing Prediction Results

Lecture 12: ARMA – Convert Stationary to Non – Stationary Data

Lecture 13: ARIMA

Lecture 14: ARIMA : Visualize the output

Chapter 11: Auto ARIMA

Lecture 1: Get best (p,d,q) with Auto ARIMA

Lecture 2: Train ARIMA with Best (p,d,q)

Lecture 3: Forecasting

Chapter 12: Appendix – Python Crash Course

Lecture 1: Install Anaconda Python

Lecture 2: Download the Resources

Lecture 3: Open Jupyter Notebook

Lecture 4: Markdown

Lecture 5: Print Statements

Instructors

Rating Distribution

  • 1 stars: 5 votes
  • 2 stars: 5 votes
  • 3 stars: 17 votes
  • 4 stars: 48 votes
  • 5 stars: 62 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!

You may also like

Email Marketing. Increase Sales With Email Marketing!

TikTok Marketing Domination : Mastering Marketing Strategies

Video Email Marketing Made Easy