Live training with Marta Markiewicz starts on July 13th at 10 AM (ET)
Training duration: 4 hours (Hands-on)
Instructor Bio:
Marta Markiewicz
SENIOR DATA SCIENTIST | LECTURER | InPost | Wrocław University of Economics and Business
Marta Markiewicz
Currently Senior (Big) Data Scientist at InPost and Lecturer at Wroclaw University of Economics and Business, previously Head of Data Science at Objectivity, with a background in Mathematical Statistics. For almost 10 years, she has been discovering the potential of data in various business domains, from medical data, through retail, HR, finance, aviation, real estate, logistics, … She deeply believes in the power of data in every area of life. Articles’ writer, conference speaker and privately – passionate dancer and hand-made jewellery creator.
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Learning Objectives
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Understand the essential theory of both basic and advanced time series models
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Build production-ready time series forecasts with python libraries
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Interpret the output of time series models to transform them into business insights
Course Abstract
Despite being not the youngest branch of data analysis, time series forecasting still poses a great challenge to both researchers and practitioners. As Niels Bohr said years ago “Prediction is very difficult, especially when it’s about the future”.
Fortunately, plurality of approaches have been proposed to address this commonly appearing challenge. This course introduces the users to the most prominent and widely used solutions, explaining their advantages and disadvantages together with tips and recommendations on the suited-for-purpose model usage.
To facilitate rapid transition of time series theory into actual business applications that students may encounter and profit from in real life, the course is equipped with hands-on code run-throughs provided in python. At the end of the course, prediction will for sure be less difficult.
Course Outline
Module 1: Time Series Introduction
- Course agenda
- Time series definition
- Real-life examples
- Example in python
Module 2: Exponential smoothing
- Single exponential smoothing
- Holt’s linear trend model
- Holt-Winters exponential smoothing
- Example in python
Module 3: (S)AR(I)MA(X)
- AR
- MA
- ARIMA
- SARIMA
- SARIMAX
- Example in python
Module 4: (Linear) regression
- Linear regression
- SVR
- Trees: Random Forests and XGBoost
- Example in python
Module 5: Neural Networks
- Artificial Neural Networks
- Recurrent Neural Networks
- LSTM
- TCN
- Example in python
Module 6: Prophet
- Prophet
- Example in python
Module 7: Performance evaluation techniques
- Time series split vs cross-validation
- Example in python
Module 8: Tricks that improve model performance
- Outliers types and removal
- Fourier series
- Hierarchical reconciliation
- Time reconciliation
Module 9: Course wrap-up
- Summary of covered methods and libraries
Which knowledge and skills you should have?
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Understand the essential theory of both basic and advanced time series models
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Build production-ready time series forecasts with python libraries
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Interpret the output of time series models to transform them into business insights
What is included in your ticket?
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Access to live training and QA session with the Instructor
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Access to the on-demand recording
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Certificate of completion