‘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.
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
Demand of products
Stock Market 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.
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
- 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?
Tuning Parameters for ARIMA
Handling Seasonality using ARIMA
Machine Learning for Time Series forecasting
Exponential Smoothing Methods
Framework to evaluate Time Series Models
- Introduction to the Course
- Introduction to Time Series
- Components of a Time Series
- AI&ML Blackbelt Plus Program (Sponsored)
- 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
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
Analytics Vidhya provides a community based knowledge portal for Analytics and Data Science professionals. The aim of the platform is to become a complete portal serving all knowledge and career needs of Data Science Professionals.
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 applicationsRead Less
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 stepsRead Less
Nice oneRead Less
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