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# Time Series Analysis in Python. Master Applied Data Analysis

## Python Time Series Analysis with 10+ Forecasting Models including ARIMA, SARIMA, Regression & Time Series Data Analysis

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## What you will learn

What is Time Series Data, it applications and components.

Fetching time series data using different methods.

Handling missing values and outliers in a time series data.

Decomposing and Splitting time series data.

Different smoothing techniques such as Simple Moving Averages, Simple Exponential, Holt and Holt-winter Exponential.

Checking Stationarity of the time series data and Converting Non-stationary to Stationary.

Auto-regressive models such as Simple AR model and Moving Average Model.

Advanced Auto-Regressive Models such as ARMA, ARIMA, SARIMA.

Evaluation Metrics used for time series data.

Rules for Choosing the Right Model for time series data.

## Description

The Ultimate course on Time Series Analysis in Python which brings you expertise in Forecasting Models, Regression, ARIMA, SARIMA and Time Series Data Analysis with Python

Do you want to know how meteorologists forecast weather?

Do you want to know how retailers reduce excess inventory and increase profit margin?

Predict the future using Time Series Forecasting!

Time series forecasting is all about looking into the future.

time series is an important field in statistical programming. It allows you to analyze:-

1. Trends

2. Seasonality

3. Irregularity

Time Series Analysis has tons of applications such as stock market analysis, pattern recognition, earthquake prediction, census analysis and many more.

Due to the advanced modern technologies, the data is growing exponentially and this data can be used to modelled for the future which can really make a big difference.

You are at the right place!

Welcome to this online resource to learn Time Series Analysis using Python.

This course begins with the basic level and goes up to the most advanced techniques step by step. Even if you do not know anything about time series, this course will make complete sense to you.

In this course you will learn about the following:-

1. What is time series data, it applications and components.

2. Fetching time series data using different methods.

3. Handling missing values and outliers in a time series data.

4. Decomposing and splitting time series data.

5. Different smoothing techniques such as simple moving averages, simple exponential, holt and holt-winter exponential.

6. Checking stationarity of the time series data and converting non-stationary to stationary.

7. Auto-regressive models such as simple AR model and moving average model.

8. Advanced auto-regressive models such as ARMA, ARIMA, SARIMA.

9. ARIMAX and SARIMAX model.

10. Evaluation metrics used for time series data.

11. Rules for choosing the right model for time series data.

All the mentioned topics will be covered theoretically as well as implemented in code.

You will compare all the models and will see how to read the results.

We will work with real data and you will have access to all the resources used in this course.

This course is for everyone who wants to master time series and become proficient in working with real life time based data.

For taking up this course you need to have prior knowledge of Python programming.

But wait!

Here is the surprise!!

If you are not aware of python programming language then also don't worry.

We have a crash course of python for you. You can take up python's crash course and then proceed with the time series analysis.

## Content

Introduction to Time Series

What is a Time Series Data

Types of Forecasting

Regression Vs Time Series

Applications of Time Series

Components of Time Series

Quiz on Introduction to Time Series Analysis

Quiz Solution on Introduction to Time Series Analysis

Time Series Analysis

Getting Time Series data

Handling Missing Values in your Time Series Data

Handling Outlier Values

Time Series Decomposition

Splitting Time Series Data

Quiz on Time Series Data Analysis

Quiz Solution on Time Series Data Analysis

Smoothing Techniques

Basic Forecasting Techniques

Metrics for Time series Forecasting

Simple Moving Averages

Simple Exponential Smoothing

Holt and Holt Winter Exponential Smoothing

Quiz on Smoothing Techniques

Quiz Solution on Smoothing Techniques

AR Models

Introduction to Auto Regressive Models

Checking for Stationarity Part 1

Checking for Stationarity using Statistical Methods Part 2

Checking for Stationary Implementation

Converting Non-Stationary Series into Stationary

Converting Non-Stationary Series into Stationary Implementation

Auto Correlation and Partial Correlation

Auto Correlation and Partial Correlation Implementation

The Simple Auto Regressive Model

The Simple Auto Regressive Model Implementation

Moving Average Model

Moving Average Model Implementation

Quiz on AR Models

Quiz Solution on AR Models

Understanding ARMA Model

Implementing ARMA Model

Understanding ARIMA Model

Implementing ARIMA Model

Understanding SARIMA Model

Implementing SARIMA Model

Quiz Solution on Advanced AR Models

ARIMAX and SARIMAX Models

Understanding ARIMAX Model

Implementing ARIMAX Model

Understanding SARIMAX Model

Implementing SARIMAX Model

Quiz on ARIMAX and SARIMAX Models

Quiz Solution on ARIMAX and SARIMAX Models

Choosing the Right Model

How to Choose the Right Model in Time Series Analysis

Choosing the Right for Model Smaller Datasets

Choosing the Right Model for Larger Datasets

Best Practices while Choosing a Time series Model

Quiz on Choosing the Right Model

Quiz Solution on Choosing the Right Model

Why do we Evaluate Performance

Why do we Evaluate Performance

Mean Forecast Error

Mean Absolute Error

Mean Absolute Percentage Error

Root Mean Squared Error

Quiz on Why do we Evaluate Performance

Quiz Solution on Why do we Evaluate Performance

Python Crash Course - Python Fundamentals

Why should you learn Python?

Installing Python and Jupyter Notebook

Naming Convention for variables

Built in Data Types and Type Casting

Scope of Variables

Quiz on Variables and Data Types

Quiz Solution

Arithmetic and Assignment Operators

Comparison, Logical, and Bitwise Operators

Identity and Membership Operators

Quiz on Operators

Quiz Solution

String Formatting

String Methods

User Input

Quiz on Strings

Quiz Solution

If, elif, and else

For and While

Break and Continue

Quiz on Loops and Conditionals

Quiz Solution

Mastering Python Data Structures

Differences between Lists and Tuples

Operations on Lists

Operations on Tuples

Quiz on Lists and Tuples

Quiz Solution

Introduction to Dictionaries

Operations on Dictionaries

Nested Dictionaries

Introduction to Sets

Set Operations

Quiz on Sets and Dictionaries

Quiz Solution

Introduction to Stacks and Queues

Implementing Stacks and Queues using Lists

Implementing Stacks andd Queues using Deque

Quiz on Stacks and Queues

Quiz Solution

Time Complexity

Linear Search

Binary Search

Bubble Sort

Insertion and Selection Sort

Merge Sort

Quiz on Searching, Sorting, and Time Complexity

Quiz Solution

Python Functions Deepdive

Introduction to Functions

Default Parameters in Functions

Positional Arguments

Keyword Arguments

Python Modules

Quiz on Introduction to Functions

Quiz Solution

Lambda Functions

Filter, Map, and Zip Functions

List, set, and Dictionary Comprehensions

Quiz on Anonymous Functions

Quiz Solution

Introduction to Aggregate Functions

Introduction to Analytical Functions

Quiz on In Built Functions

Quiz Solution

Solving the Factorial Problem using Recursion

Solving the Fibonacci Problem using Recursion

Quiz on Recursions

Quiz Solution

Introduction to Classes and Objects

Inheritance

Encapsulation

Polymorhism

Quiz on Classes and Objects

Quiz Solution

Python for Data Science

Introduction to datetime

The date and time class

The datetime class

The timedelta class

Quiz on Dates and Times

Quiz Solution

Meta Characters for Regular Expressions

Built-in Functions for Regular Expressions

Special Characters for Regular Expressions

Sets for Regular Expressions

Quiz on Regular Expressions

Quiz Solution

Array Creation using Numpy

Mathematical Operations using Numpy

Built-in Functions in Numpy

Quiz on Introduction to Numpy

Quiz Solution

Plotting Data in Pandas

Indexing, Selecting, and Filtering Data using Pandas

Merging and Concatenating DataFrames

Lambda, Map, and Apply Functions

Quiz on Introduction to Pandas

Quiz Solution

## Reviews

A
Akash27 April 2021

One of my friend suggested this course and I have done many courses on this topic but I found this one very well explained and I suggest everyone should buy this and learn which will help you improve your knowledge.

V
Venkateswara27 April 2021

This course is awesome. This course covers the Time Series and forecasting techniques, especially for beginners in time series analysis and for knowledge gaps of TSA. The presentation is really good and also the presenter is quite capable of explaining all the concepts in an easy to understand manner. Highly recommended for everyone who wishes to learn about time series modules.

Z
Zehra26 April 2021

The course brings what it promises. This course is good to get a grasp on financial Time Series forecasting. I have learned a lot in a short time, very interesting and useful. I will continue learning a lot more about time series with Python. Good explanation about the use of the models. An excellent instructor, the class was informative. It was a brilliant course none the less.

C

The entire course is easy to understand and quick to grasp in. Brilliant explanation followed by right amount of quizzes with exact solutions made it more interesting. The instructor dealt every topic with utmost clarity. I personally learned a lot by this course and most importantly, I am thankful for the best set of knowledge provided which further opened the gateways to go beyond with my skill set and confidence.

A
Azeemunnisa26 April 2021

One of the best courses about Time Series analysis in python. The lessons are clear and helps us understand the concepts easily with provided exercises. Detail information on the topics which are suitable to beginners to experts, I really recommend you to go check the course and learn from the best foundation.

S
Syed22 April 2021

What a great course!!! I received a Time Series project at work, so I finished the course in 4 days and did a hands-on study of my project data, and I cannot tell you how wonderful it is that this course was at least at a level to grasp the time series, so that respectable outcomes for the project could be initiated. I look forward to more of this. Thank you Aditi

V
VISHAL20 April 2021

I have taken several courses and I have found the instructors to be professional and knowledgeable. The content has been engaging and cause reflection and opportunities to practice.

V
Venkatesh20 April 2021

Great Introduction to Time Series forecasting. The course is a really good mix of theory and practice. The exercises and solutions are so practical. This course gives a good practical introduction to Time Series Analysis. The basic concepts in the Time series are explained well without overwhelming the learner. Thank you!! I recommend it.

N
Nazir14 April 2021

This course provides you with an understanding of all the concepts of time series. It covers almost everything including Python concepts. I would like to thank Aditi for making this course so easy. You are simply the best. I highly recommend everyone who is interested in building a strong foundation on Time Series Analysis with Python.

D
Dr14 April 2021

This is an excellent python course. Aditi is a great teacher and has explained concepts very well. I did a few other time series analysis courses in the past but I lost in the end. Every subject was discussed here with extreme clarity by the instructor. I highly recommend this course. Thank you very much!!

H
Harika14 April 2021

For many reasons, I choose this course over several other related courses. Instead of wasting time on time series with profound learning, this course explores a broad variety of classic approaches. Either you follow the classical methods or go deeply into the learning process, it is useful to study many of the classical methods so you have a wider base for thinking and can read article in classical methods. The course is, however, very good description and well prepared course material.

P
Pooja14 April 2021

Wonderful and super course! This one seems to be of the same great quality. Aditi will Challenge you to actually use what she teaches you many times along the way. You will know how to use Time Series Analysis when you finish this course! I have got a clear understanding of Time Series concepts along with Python. Definitely recommended to beginners and intermediate.

S
Syed14 April 2021

This is one of Udemy's finest courses I have ever taken. The exercises and concepts are really handy. In Time Series Analysis, I took a college class but I did not quite grasp the principles. During this course I certainly learned a lot. And, above all, thanks for opening new doors that go beyond this range of capabilities. This is what I am all about.  This course helped to understand TSA much better way. A huge thank you to the developers. Overall, it's been a very nice course. I now understand the fundamentals of time series modelling and predictions (using ARIMAX, SARIMAX).

S
Shishir13 April 2021

This is an amazing course. Kudos to the makers of the course. I didn't have prior knowledge of time series forecasting but now I am pretty confident on the concepts of time series. Thank you for such a great content. I definitely recommend this course if you want to become familiar with time series forecasting.

G
G13 April 2021

I love this course!! I hugely recommend this course if you want to become familiar with time series forecasting. Really thank you for a tremendous course. it is very thorough and challenging and helps me stretch the limits of not only my time series skills, but also my overall logical thinking. Thank you Aditi for this course. You're an amazing teacher.

## Coupons

DateDiscountStatus
5/3/2021100% OFFExpired

Udemy ID

## 3/15/2021

Course created date

## 4/29/2021

Course Indexed date
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