Time Series Analysis

Course code: MLTSA

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

170 EUR

206 EUR including VAT

The earliest date from 28.03.2023

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Course dates

Starting date: 28.03.2023

Type: In-person/Virtual

Course duration: 1 day

Language: en/cz

Price without VAT: 170 EUR

Register

Starting date: Individual

Type: In-person/Virtual

Course duration: 1 day

Language: en/cz

Price without VAT: 170 EUR

Register

Starting
date
Place
Type Course
duration
Language Price without VAT
28.03.2023 In-person/Virtual 1 day en/cz 170 EUR Register
Individual In-person/Virtual 1 day en/cz 170 EUR Register
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Course structure

  • 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)

Prerequisites

  • 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

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