What is Time Series Analysis?

‘Time’ is the most important factor which ensures success in a business. It’s difficult to keep up with the pace of time.  But, technology has developed some powerful methods using which we can ‘see things’ ahead of time!

Nope, not the time machine, we are talking about the methods of prediction & forecasting. As the name ‘time series forecasting’ suggests, it involves working on time (years, days, hours, minutes) based data, to derive hidden insights to make informed decision making.

Importance of Time Series Analysis

Time series models are very useful models when you have serially correlated data as shown above. Most businesses work on time series data to analyze 

  • Sales numbers for the next year 

  • Website Traffic

  • Competition Position

  • Demand of products

  • Stock Market Analysis

  • Census Analysis

  • Budgetary Analysis


This is just the tip of the iceberg and there are numerous prediction problems that involve a time component and concepts of time series analysis come into picture.

Why is Time Series Forecasting Challenging?

But what makes a time series more challenging than say a regular regression problem? There are 2 things:

  • Time Dependence of a time series - The basic assumption of a linear regression model that the observations are independent doesn’t hold in this case.
  • Seasonality in a time series - Along with an increasing or decreasing trend, most time series have some form of seasonal trends, i.e. variations specific to a particular time frame. 


The above challenges motivated us to build a hands on course explaining the implementation of various time series forecasting methods using Python

What do I need to start Twitter Sentiment Analysis course?

  • A working laptop / desktop with 4 GB RAM
  • A working Internet connection
  • Basic knowledge of Machine Learning
  • Basic knowledge of Python - check out this Course
  • first, if you are new to Python
  • Python libraries you need for completing the time series forecasting project: sklearn, pandas, statsmodels

This is all it takes for you to take your first step with Time Series forecasting using Python.
What are you waiting for?
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What do I need to start Twitter Sentiment Analysis course?

What will you learn from Time Series Forecasting using Python Course?

This course is designed for people who want to solve problems related to Time Series Forecasting. By the end of the course, you will learn to apply the following necessary skills and techniques required to solve Time Series problems:

  • ARIMA Model

  • Tuning Parameters for ARIMA

  • Handling Seasonality using ARIMA

  • Moving Average

  • Machine Learning for Time Series forecasting

  • Exponential Smoothing Methods

  • Framework to evaluate Time Series Models

Course curriculum

  • 1
    Time Series Analysis
    • Introduction to Time Series
    • Components of a Time Series
    • Problem Statement
    • Table of Contents
    • Hypothesis Generation
    • Getting the system ready and loading data
    • Dataset Structure and Content
    • Feature Extraction
    • Exploratory Analysis
    • Exercise 1
    • Splitting the data into training and validation part
    • Modeling Techniques
    • Holt's Linear trend model on daily time series
    • Holt Winter's model on daily time series
    • Introduction to ARIMA model
    • Parameter tuning for ARIMA model
    • SARIMAX model on daily time series
    • Exercise 2
    • Important Links
    • Your Feedback
  • 2
    Introduction to Time Series
    • Introduction to the Course
    • Introduction to Time Series
    • Components of a Time Series
  • 3
    Understanding Problem Statements and Data Sets
    • Problem Statement
    • Table of Contents
    • Hypothesis Generation
    • Getting the system ready and loading data
    • Dataset Structure and Content
  • 4
    Exploration and Preprocessing
    • Feature Extraction
    • Exploratory Analysis
    • Exercise 1
  • 5
    Modelling Techniques and Evaluation
    • Splitting the data into training and validation part
    • Modeling Techniques
    • Holt's Linear trend model on daily time series
    • Holt Winter's model on daily time series
    • Introduction to ARIMA model
    • Parameter tuning for ARIMA model
    • SARIMAX model on daily time series
    • Exercise 2
    • Important Links
    • Your Feedback

What do you get as a part of the Time Series Forecasting using Python course?

This course is divided into 3 sections:

  • Understanding Time Series Analysis
  • Data Exploration for Time Series
  • Time Series Forecasting using different methods

These sections are supplemented with theory, coding examples and exercises. Additionally, you will be provided with the below resources: 

  • Time Series Datasets  Dataset from a real-life industry time series use case
  • Jupyter Notebooks  Fully functioning Python codes for understanding the data and later building models for performing time series forecasting


Here's what our students have to say about our Creating Time Series Forecast using Python course

  • very good course!

    yuchen xiao

    This is a very good course for beginners on time series analysis. Very approachable, with a good balance between concept and real applications

    This is a very good course for beginners on time series analysis. Very approachable, with a good balance between concept and real applications

    Read Less
  • Wonderful way of explaining

    Surya Rasp

    I tried many courses, but this one just addresses required minimum basics( no more formula over loading) with practical example, little data preparation like...

    Read More

    I tried many courses, but this one just addresses required minimum basics( no more formula over loading) with practical example, little data preparation like normalisation , taking example of Non stationary and converting to stationary might covered all steps

    Read Less
  • Review

    Krinza Momin

    Excellent!

    Excellent!

    Read Less
  • Creating Time Series Forecast using Python

    Rajeswar Rao ippala

    Amazing

    Amazing

    Read Less
  • Good

    HAFSA BALOCH

    Good

  • Great Course

    AMAR KUMAR

    Nice one

    Nice one

    Read Less

Frequently Asked Questions

Customer Questions about Time Series Forecasting

  • Who should take this course?

    This course is meant for people looking to explore Time Series Forecasting in Python.

  • Do I need to install any software before starting the course?

    You will need to download and install python.

  • What is the refund policy?

    The course is free of charge.

  • Do I need to take the modules in a specific order?

    We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it.

  • Do I get certificate upon completion of the course?

    This is a free course and therefore there is no certificate involved.

  • What is the fee for this course?

    The course is free of charge.

  • How long I can access the course?

    You will have access to the course for a duration of 6 months.

  • Is there any placement support

    This is an introductory course and this does not include any placement support. Once you have worked on a few data science projects and hackathons, you can always apply to jobs on Analytics Vidhya portal

Enroll in Time Series Forecasting using Python today

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