Linear Models and Time Series Forecasting

Linear regression, nonlinear regression, auto-regressive models, time series analysis, regularisation and more


What you will learn


You will learn the skills you need to develop and evaluate regression models that allow you to make quantitative predictions.

Multivariate linear regression
Autogression Models, ARMA, Prophet
Logistic Regression

Languages and libraries :

Python 3
Numpy and Pandas for data manipulation
Scikit-learn and statsmodel for linear and time series model
Matplotlib for visualisation


Good knowledge of python, basic understanding of machine learning practice.


Those who wish to take their data science skills further and learn state-of-the-art techniques in this constantly evolving field.

Get in touch with us to learn more about the course! 


Regression Analysis


Time Series Analysis

Case Studies

Learning  Outcomes:

Get familiar with the theory of linear regression and how to implement the algorithm
Understand the theory behind logistic regression, how and when to use it in practice
Be able to work with time and date objects in Python, pandas and numpy
Learn how to process time-series data to prepare for analysis and forecasting
Learn the theory behind autoregression model and use ARMA in practice
Learn how to properly evaluate time-series models
Be able to use the Prophet library on time-series projects

KATE Projects:

  • Classification and Model Selection: Build a supervised learning model to predict the success of Kickstarter campaigns.
  • Time Series Analysis: Forecasting of daily energy consumption in London.

Get in Touch 

Interested in learning more?

If you’re interested in what the ‘Linear Models and Time Series Forecasting’ module could do for your team or department, please complete the form to the right of this text and we’ll get back to you within two working days with more information.

Get in touch now

Please complete all of the required fields to get in touch with us. Alternatively, call +44(0)7816 419378 or email now