Time Series Analysis and Forecasting using Python

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

4.59 (1200 reviews)
Udemy
platform
English
language
Data Science
category
138,228
students
13.5 hours
content
Jan 2022
last update
$29.99
regular price

What you will learn

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

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 Forecasting and Time Series Analysis course using Python Time Series techniques. This course teaches you everything you need to know about different time series forecasting and time series analysis models and how to implement these models in Python time series.

After completing this course you will be able to:

  • Implement time series forecasting and time series analysis models such as AutoRegression, Moving Average, ARIMA, SARIMA etc.

  • Implement multivariate time series forecasting models based on Linear regression and Neural Networks.

  • Confidently practice, discuss and understand different time series forecasting, time series analysis models and Python time series techniques used by organizations

How will this course help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Time Series Forecasting course on time series analysis and Python time series applications.

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. You will also learn time series forecasting models, time series analysis and Python time series techniques.

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, time series forecasting and time series analysis

  • Step-by-step instructions on implement time series forecasting models in Python

  • Downloadable Code files containing data and solutions used in each lecture on time series forecasting, time series analysis and Python time series techniques

  • Class notes and assignments to revise and practice the concepts on time series forecasting, time series analysis and Python time series techniques


The practical classes where we create the model for each of these strategies is something which differentiates this course from any other available online course on time series forecasting, time series analysis and Python time series techniques.

.What makes us qualified to teach you?

  • The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Analytics and we have used our experience to include the practical aspects of Marketing and data analytics in this course. They also have an in-depth knowledge on time series forecasting, time series analysis and Python time series techniques.

We are also the creators of some of the most popular online courses - with over 170,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts on time series forecasting, time series analysis and Python time series techniques.

Each section contains a practice assignment for you to practically implement your learning on time series forecasting, time series analysis and Python time series techniques.

What is covered in this course?

Understanding how future sales will change is one of the key information needed by manager to take data driven decisions. In this course, we will deal with time series forecasting, time series analysis and Python time series techniques. We will also explore how one can use forecasting models to

  • See patterns in time series data

  • Make forecasts based on models

Let me give you a brief overview of the course

  • Section 1 - Introduction

In this section we will learn about the course structure and how the concepts on time series forecasting, time series analysis and Python time series techniques will be taught in this course.

  • Section 2 - Python basics

This section gets you started with Python.

This section will help you set up the python and Jupyter environment on your system and it'll teach

you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

The basics taught in this part will be fundamental in learning time series forecasting, time series analysis and Python time series techniques on later part of this course.

  • Section 3 - Basics of Time Series Data

In this section, we will discuss about the basics of time series data, application of time series forecasting, and the standard process followed to build a forecasting model, time series forecasting, time series analysis and Python time series techniques.

  • Section 4 - Pre-processing Time Series Data

In this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models and execute time series forecasting, time series analysis and implement Python time series techniques.

  • Section 5 - Getting Data Ready for Regression Model

In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.

We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation.

  • Section 6 - Forecasting using Regression Model

This section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results.

  • Section 7 - Theoretical Concepts

This part will give you a solid understanding of concepts involved in Neural Networks.

In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

  • Section 8 - Creating Regression and Classification ANN model in Python

In this part you will learn how to create ANN models in Python.

We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.

I am pretty confident that the course will give you the necessary knowledge and skills related to time series forecasting, time series analysis and Python time series techniques to immediately see practical benefits in your work place.

Go ahead and click the enroll button, and I'll see you in lesson 1 of this course on time series forecasting, time series analysis and Python time series techniques!

Cheers

Start-Tech Academy

Screenshots

Time Series Analysis and Forecasting using Python - Screenshot_01Time Series Analysis and Forecasting using Python - Screenshot_02Time Series Analysis and Forecasting using Python - Screenshot_03Time Series Analysis and Forecasting using Python - Screenshot_04

Content

Introduction

Introduction

Time Series - Basics

Time Series Forecasting - Use cases
Course Resources
Forecasting model creation - Steps
Forecasting model creation - Steps 1 (Goal)
Time Series - Basic Notations

Setting up Python and Python Crash Course

Installing Python and Anaconda
Course resources
Opening Jupyter Notebook
Introduction to Jupyter
Arithmetic operators in Python: Python Basics
Strings in Python: Python Basics
Lists, Tuples and Directories: Python Basics
Working with Numpy Library of Python
Working with Pandas Library of Python
Working with Seaborn Library of Python

Time Series - Data Loading

Data Loading in Python

Time Series - Visualization

Time Series - Visualization Basics
Time Series - Visualization in Python

Time Series - Feature Engineering

Time Series - Feature Engineering Basics
Time Series - Feature Engineering in Python

Time Series - Resampling

Time Series - Upsampling and Downsampling
Time Series - Upsampling and Downsampling in Python

Time Series - Transformation

Time Series - Power Transformation
Moving Average
Exponential Smoothing

Time Series - Important Concepts

White Noise
Random Walk
Decomposing Time Series in Python
Differencing
Differencing in Python

Time Series - Test Train Split

Test Train Split in Python

Time Series - Naive (Persistence) model

Naive (Persistence) model in Python

Time Series - Auto Regression Model

Auto Regression Model - Basics
Auto Regression Model creation in Python
Auto Regression with Walk Forward validation in Python

Time Series - Moving Average model

Moving Average model -Basics
Moving Average model in Python

Time Series - ARIMA model

ACF and PACF
ARIMA model - Basics
ARIMA model in Python
ARIMA model with Walk Forward Validation in Python

Time Series - SARIMA model

SARIMA model in Python

Linear Regression - Data Preprocessing

Additional Course Resources
Gathering Business Knowledge
Data Exploration
The Dataset and the Data Dictionary
Importing Data in Python
Univariate analysis and EDD
EDD in Python
Outlier Treatment
Outlier Treatment in Python
Missing Value Imputation
Missing Value Imputation in Python
Seasonality in Data
Bi-variate analysis and Variable transformation
Variable transformation and deletion in Python
Non-usable variables
Dummy variable creation: Handling qualitative data
Dummy variable creation in Python
Correlation Analysis
Correlation Analysis in Python

Linear Regression - Model Creation

The Problem Statement
Basic Equations and Ordinary Least Squares (OLS) method
Assessing accuracy of predicted coefficients
Assessing Model Accuracy: RSE and R squared
Simple Linear Regression in Python
Multiple Linear Regression
The F - statistic
Interpreting results of Categorical variables
Multiple Linear Regression in Python
Test-train split
Bias Variance trade-off
Test train split in Python

Introduction to ANN

Introduction to Neural Networks and Course flow

Single Cells - Perceptron and Sigmoid Neuron

Perceptron
Activation Functions
Python - Creating Perceptron model

Neural Networks - Stacking cells to create network

Basic Terminologies
Gradient Descent
Back Propagation

Important concepts: Common Interview questions

Some Important Concepts

Standard Model Parameters

Hyperparameters

Tensorflow and Keras

Keras and Tensorflow
Installing Tensorflow and Keras

Python - Dataset for classification problem

Dataset for classification
Normalization and Test-Train split

Python - Building and training the Model

Different ways to create ANN using Keras
Building the Neural Network using Keras
Compiling and Training the Neural Network model
Evaluating performance and Predicting using Keras

Python - Solving a Regression problem using ANN

Building Neural Network for Regression Problem

Reviews

Akshay
March 2, 2022
The lecturers just state facts without giving any explanation of why this is hapenning. For example in AR, they could have just added a simple equation expressing the model. This would have helped. Also in the AR video on python, they just mention 29 lag values... What does that even mean?
Jefry
December 27, 2021
The course is well-structured and explained, it is easy to understand and follow, except for the accent of the instructors, that really complicate the explanation in different points of the course.
Lautaro
December 10, 2021
Misleading Title, only the first part (till SARIMAX) is about Time Series. Then the linear regression and the NN, there is no relationship at all with Forecasting. Completely waste of money, if you are looking to advanced machine learning techniques with Time series, do not buy this course.
SNEHAL
November 30, 2021
It is a great to start learning time series from basic. I am wishing to complete this course soon and learning time series in depth.
Ajay
October 29, 2021
The course is very good. I just loved this course till now and this was the only course that i was looking for because it helps to analyze time series data and helps to predict the future outcomes. I'll be more than happy if i successfully achieve this skill through this course.
Syauqi
September 27, 2021
Good material, good examples, but I think it's too lengthy. The words needs to be kept simple but comprehensible.
Tobias
August 12, 2021
Actually showing Python functions that go beyond some basics not shown in the other videos ive seen so far.
Ramya
July 18, 2021
your teaching of way is excellent, clear explanation in every topics.. I learn many topics is easily understanding... I like and very interesting to learn.. awesome!
Juan
July 12, 2021
La parte de Series de Tiempo ARIMA, SARIMA y SARIMAX tiene errores en su código y su explicación es confusa.
Tássio
July 2, 2021
It went through most of the topics I was hoping for, although the neural networks section did not have examples for time series cases. hopefully will be added in the future.
Akshar
July 1, 2021
The course covers great details about regression and time series. But lacks up-to-date content and video quality in middle.
Dhanalakshmi.S
June 17, 2021
The course is really interesting. I learnt many new concepts. But towards the end, time series concepts faded out and concepts related to neural network and normal Machine learning comes into picture actually i required detailed discussion on time series concepts. Thank you
nash
May 16, 2021
In Re sampling video need to be modified not understanble of exact way of using the different scenarios
Chillara
May 2, 2021
The way content was delivered is amazing and all the core concepts are covered in this course. Enjoyed this course throughout the lecture.
Vamshikrishna
March 22, 2021
Special thanks to the course creator. With zero knowledge on time series I started taking this course, Now I am in good position and doing projects related to time series. A very recommended and worthful course in Udemy. Really appreciated in way of designing the course started basics then intermediate handling, plotting then models from scratch.

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2859872
udemy ID
3/9/2020
course created date
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