4.22 (601 reviews)
☑ 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
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.
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
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
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. Each section contains a practice assignment for you to practically implement your learning.
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 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
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.
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
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
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 to immediately see practical benefits in your work place.
Go ahead and click the enroll button, and I'll see you in lesson 1
Time Series - Basics
Time Series Forecasting - Use cases
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
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
Time Series - Important Concepts
Decomposing Time Series in Python
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
The Dataset and the Data Dictionary
Importing Data in Python
Univariate analysis and EDD
EDD in Python
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
Dummy variable creation: Handling qualitative data
Dummy variable creation in Python
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
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
Python - Creating Perceptron model
Neural Networks - Stacking cells to create network
Important concepts: Common Interview questions
Some Important Concepts
Standard Model Parameters
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
Awful English, moreover all the regression and deep learning part is not related to time series! To avoid
I learned new things in this course that i could apply to real world problems and deeply understand the concept, requirements and working of Time Series Models.
The course started strong however by the time I got to lecture 41, there were a ton of mistakes in the lecture. Also, some of their work is outdated as the code provided by them doesn't work for ARIMA. The course is well organized but with so many mistakes, it's hard to focus.
Good practical implementation based course with all of the necessities of python covered along with one of the most difficult topics to understand
Results of the regression model created using ANN were not nicely explained, the Time series part was very good.
This course is really Awesome, very well explained the Time Series concept along with fundamental logic and concept of Data Science calculation and neural network. I highly recommend this course to everyone. Really thanks for such a valuable course. thanks Udemy and team.
The course is great, and it briefly touches on the mathematical background of the used algorithms which I believe is essential. It would be better to add more quizzes to make sure learners fully understood the concepts.
I am not getting any new knowledge so far. Yes, this course gave me direction of thinking when you start on any problem.
up to this stage, the introduction and the objectives of the course have been introduced along with some conceptual facts necessary for the analysis. The presentation refers to the Amtrak dataset which is not available with the coursee materials.
Great course! Lecturers have a lot of knowledge and skillfully transfer it. The scope of the course is broader than the topic indicates. My expectations were exceeded.
In starting of the course. I cant say anything but i like te introduction in simple way application of time series forcasting is expalin
I have enjoyed this course as I get to review time series analysis (and I was expecting to see ANN apply into time series analysis). Also, it helped to introduce ANN with Keras over Tensorflow for classification or regression problems. Many thanks
Esperaba se utilizaran pronosticos con las redes neuronales y no la repeticion de otro curso que ya habia tomado de su parte.
The Indian accent of this guy is almost impossible to understand. The closed caption and subtitles are frequently incorrect. The material is good and the guy has the knowledge but he should not be giving a course in English language, he should be teaching in Urdu or Bengali.
It was a good overview of AR, ARMA, etc and regression was taught beautifully, although I had a hard time with the accents at a few points. Why 3 stars? I was hoping on seeing how to apply a real-time series of data to the model, for example, as would be presented by a series of sensors. In other words the actual keras/TF portion was based on training standard canned datasets, not on a time-series (which is what I need.) Further, once the model is set up, how do I use it to find anomalies in incoming data streams (when actual inputs are different than the predicted)?