Time Series Analysis and Forecasting using Python

Learn about time series analysis & forecasting models in Python |Time Data Visualization|AR|MA|ARIMA|Regression| ANN

Created by Abhishek And Pukhraj, Last Updated 15-Apr-2020, Language:English

What Will I Get ?

Get a solid understanding of Time Series Analysis and Forecasting
Understand the business scenarios where Time Series Analysis is applicable
Building 5 different Time Series Forecasting Models in Python
Learn about Auto regression and Moving average Models
Learn about ARIMA and SARIMA models for forecasting
Use Pandas DataFrames to manipulate Time Series data and make statistical computations.

Requirements

Students will need to install Python and Anaconda software but we have a separate lecture to help you install the sameStudents will need to install Python and Anaconda software but we have a separate lecture to help you install the same

Description

You're looking for a complete course on Time Series Forecasting to drive business decisions involving production schedules, inventory management, manpower planning, and many other parts of the business., right?

You've found the right Time Series Analysis and Forecasting course. This course teaches you everything you need to know about different forecasting models and how to implement these models in Python.

After completing this course you will be able to:

Implement time series forecasting models such as AutoRegression, Moving Average, ARIMA, SARIMA etc.
Implement multivariate forecasting models based on Linear regression and Neural Networks.
Confidently practice, discuss and understand different Forecasting models used by organizations

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Marketing Analytics: Forecasting Models with Excel course.

If you are a business manager or an executive, or a student who wants to learn and apply forecasting models in real world problems of business, this course will give you a solid base by teaching you the most popular forecasting models and how to implement it.

Why should you choose this course?

We believe in teaching by example. This course is no exception. Every Section’s primary focus is to teach you the concepts through how-to examples. Each section has the following components:

Theoretical concepts and use cases of different forecasting models
Step-by-step instructions on implement forecasting models in Python
Downloadable Code files containing data and solutions used in each lecture
Class notes and assignments to revise and practice the concepts

The practical classes where we create the model for each of these strategies is something which differentiates this course from any other course available online.

Who this course is for:

People pursuing a career in data science
Working Professionals beginning their Machine Learning journey
Statisticians needing more practical experience
Anyone curious to master Time Series Analysis using Python in short span of time

Course Content

Introduction

1 Lectures 00:02:10
- Introduction
  
  Preview
  
  00:02:10
Time Series - Basics

5 Lectures 00:20:16
- Time Series Forecasting - Use cases
  
  Preview
  
  00:02:25
- Course Resources
- Forecasting model creation - Steps
  
  Preview
  
  00:02:46
- Forecasting model creation - Steps 1 (Goal)
  
  00:06:03
- Time Series - Basic Notations
  
  00:09:02
Setting up Python and Python Crash Course

9 Lectures 01:37:58
- Installing Python and Anaconda
  
  00:03:04
- Opening Jupyter Notebook
  
  00:09:06
- Introduction to Jupyter
  
  00:13:26
- Arithmetic operators in Python: Python Basics
  
  Preview
  
  00:04:28
- Strings in Python: Python Basics
  
  00:19:07
- Lists, Tuples and Directories: Python Basics
  
  00:18:41
- Working with Numpy Library of Python
  
  00:11:54
- Working with Pandas Library of Python
  
  00:09:15
- Working with Seaborn Library of Python
  
  00:08:57
Time Series - Data Loading

1 Lectures 00:17:51
- Data Loading in Python
  
  00:17:51
Time Series - Visualization

2 Lectures 00:36:38
- Time Series - Visualization Basics
  
  00:09:28
- Time Series - Visualization in Python
  
  00:27:10
Time Series - Feature Engineering

2 Lectures 00:29:04
- Time Series - Feature Engineering Basics
  
  00:11:03
- Time Series - Feature Engineering in Python
  
  00:18:01
Time Series - Resampling

2 Lectures 00:21:02
- Time Series - Upsampling and Downsampling
  
  00:04:17
- Time Series - Upsampling and Downsampling in Python
  
  00:16:45
Time Series - Transformation

3 Lectures 00:11:51
- Time Series - Power Transformation
  
  00:02:32
- Moving Average
  
  00:07:12
- Exponential Smoothing
  
  00:02:07
Time Series - Important Concepts

5 Lectures 00:37:56
- White Noise
  
  00:02:29
- Random Walk
  
  Preview
  
  00:04:23
- Differencing
  
  00:06:16
- Decomposing Time Series in Python
  
  00:09:41
- Differencing in Python
  
  00:15:07
Time Series - Test Train Split

1 Lectures 00:11:28
- Test Train Split in Python
  
  00:11:28
Time Series - Naive (Persistence) model

1 Lectures 00:07:54
- Naive (Persistence) model in Python
  
  00:07:54
Time Series - Auto Regression Model

3 Lectures 00:21:11
- Auto Regression Model - Basics
  
  00:03:29
- Auto Regression Model creation in Python
  
  00:09:22
- Auto Regression with Walk Forward validation in Python
  
  00:08:20
Time Series - Moving Average model

2 Lectures 00:13:31
- Moving Average model -Basics
  
  00:04:33
- Moving Average model in Python
  
  00:08:58
Time Series - ARIMA model

4 Lectures 00:31:29
- ACF and PACF
  
  00:08:07
- ARIMA model - Basics
  
  00:04:43
- ARIMA model in Python
  
  00:13:15
- ARIMA model with Walk Forward Validation in Python
  
  00:05:24
Time Series - SARIMA model

2 Lectures 00:18:06
- SARIMA model
  
  00:07:26
- SARIMA model in Python
  
  00:10:40
Stationary time Series

1 Lectures 00:01:42
- Stationary time Series
  
  00:01:42
Linear Regression - Data Preprocessing

20 Lectures 02:07:09
- Introduction
  
  00:02:20
- Additional Course Resources
- Gathering Business Knowledge
  
  00:03:26
- Data Exploration
  
  00:03:19
- The Dataset and the Data Dictionary
  
  00:07:31
- Importing Data in Python
  
  00:06:03
- Univariate analysis and EDD
  
  00:03:33
- EDD in Python
  
  00:12:11
- Outlier Treatment
  
  00:04:15
- Outlier Treatment in Python
  
  00:14:18
- Missing Value Imputation
  
  00:03:36
- Missing Value Imputation in Python
  
  00:04:57
- Seasonality in Data
  
  00:03:34
- Bi-variate analysis and Variable transformation
  
  00:16:14
- Variable transformation and deletion in Python
  
  00:09:21
- Non-usable variables
  
  00:04:44
- Dummy variable creation: Handling qualitative data
  
  00:04:50
- Dummy variable creation in Python
  
  00:05:45
- Correlation Analysis
  
  00:10:05
- Correlation Analysis in Python
  
  00:07:07
Linear Regression - Model Creation

12 Lectures 01:44:11
- The Problem Statement
  
  00:01:25
- Basic Equations and Ordinary Least Squares (OLS) method
  
  00:08:13
- Assessing accuracy of predicted coefficients
  
  00:14:40
- Assessing Model Accuracy: RSE and R squared
  
  00:07:19
- Simple Linear Regression in Python
  
  00:14:06
- Multiple Linear Regression
  
  00:04:57
- The F - statistic
  
  00:08:22
- Interpreting results of Categorical variables
  
  00:05:04
- Multiple Linear Regression in Python
  
  00:14:13
- Test-train split
  
  00:09:32
- Bias Variance trade-off
  
  00:06:01
- Test train split in Python
  
  00:10:19
Introduction to ANN

1 Lectures 00:04:38
- Introduction to Neural Networks and Course flow
  
  00:04:38
Single Cells - Perceptron and Sigmoid Neuron

3 Lectures 00:31:27
- Perceptron
  
  00:09:47
- Activation Functions
  
  00:07:30
- Python - Creating Perceptron model
  
  00:14:10
Neural Networks - Stacking cells to create network

3 Lectures 00:44:31
- Basic Terminologies
  
  00:09:47
- Gradient Descent
  
  00:12:17
- Back Propagation
  
  00:22:27
Important concepts: Common Interview questions

1 Lectures 00:12:44
- Some Important Concepts
  
  00:12:44
Standard Model Parameters

1 Lectures 00:08:19
- Hyperparameters
  
  00:08:19
Tensorflow and Keras

2 Lectures 00:07:08
- Keras and Tensorflow
  
  00:03:04
- Installing Tensorflow and Keras
  
  00:04:04
Python - Dataset for classification problem

2 Lectures 00:13:18
- Dataset for classification
  
  00:07:19
- Normalization and Test-Train split
  
  00:05:59
Python - Building and training the Model

4 Lectures 00:34:17
- Different ways to create ANN using Keras
  
  00:01:58
- Building the Neural Network using Keras
  
  00:12:24
- Compiling and Training the Neural Network model
  
  00:10:34
- Evaluating performance and Predicting using Keras
  
  00:09:21
Python - Solving a Regression problem using ANN

1 Lectures 00:22:10
- Building Neural Network for Regression Problem
  
  00:22:10

Abhishek And Pukhraj

Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.
Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey.

Founded by Abhishek Bansal and Pukhraj Parikh.

Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.

Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.