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Time Series

This course is focused to time series prediction problem.
Level
Designed for participants with basic knowledge and experience
intermediate
Course length
1 day
Language
 cz  eu
Course code
PU21110287
Machine Learning
Category:
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Courses with lecturer

Term
Language
Place
Form
?
How and where the course takes place.
Price without VAT
20. 10. 2026 09:00 - 17:00
Language
Place
Praha
Form
classroom
?
The course with an instructor in classroom.
Code of the course: PU21110287-0025
Price without VAT
4 990 Kč
20. 10. 2026 09:00 - 17:00
Language
Place
online
Form
virtual classroom
?
Online training with a lecturer at a specific time.
Code of the course: PU21110287-0026
Price without VAT
4 990 Kč
Open term
?
We will agree on a specific date together. This is a non-binding order.
Language
Place
Praha
Form
classroom
?
The course with an instructor in classroom.
Code of the course: PU21110287-0003
Price without VAT
4 990 Kč
Open term
?
We will agree on a specific date together. This is a non-binding order.
Language
Place
Praha
Form
classroom
?
The course with an instructor in classroom.
Code of the course: PU21110287-0004
Price without VAT
4 990 Kč

Course description

We begin with examples of classical methods for modeling and prediction of time series and we continue to more advanced methods based on machine learning. We finish with a complex example of training time series model on historical data using neural network and we evaluate its performance in predicting future.

Required knowledge

  • basic knowledge of programing in Python
  • high school level of mathematics
  • Basics of machine learning on the level of our course Introduction to  machine Learning

Course content

  • Introduction to the theory of time series modeling
  • Classical methods for time series prediction (space & frequency domain, spectral analysis, autocorrelation, ARIMA models etc.)
  • Hands-on example (pandas, basic characteristics, simple prediction)
  • Machine learning for time series prediction (state-space methods, Hidden Markov Chain, Kalman filter, classical neural networks, recurrent networks, LSTM)
  • Hands-on examples of machine learning methods (training set preparation for specific task and model, training process & evaluation)
  • Complex example of time series prediction using recurrent neural network (temperature prediction from high-dimensional input data: training data set preparation, training process & validation, prediction with trained neural network)

Lecturers

Dušan Fedorčák
Dušan Fedorčák

He has been working in the field of machine learning for more than 10 years. During his time in academia, he dealt with self-organization, machine learning without a teacher, time series prediction and traffic modeling. Since 2014, he has been on the startup scene (GoodAI - research of general artificial intelligence, Neuron Soundware - sound processing, CEAI - fintech & NLP). 

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Previous courses

Do you want this tailor-made course for your company?

Contact us

News with the course

Náhledový obrázek novinky
Artificial intelligence (AI) 15. 11. 2024
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HubSpot is an American software development company. It has 8,000 employees and branches in several countries around the world. Processes related to lead generation and qualification were time-consuming and not always efficient, reducing sales productivity.

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DHL is one of the largest logistics companies in the world. It provides transportation and delivery of parcels in many countries. However, route planning, warehouse management and demand forecasting were difficult and often inefficient.

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