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Introduction to Time Series Forecasting with Python

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Faculty of Computer and Information Science

113 Večna pot

Lecture Room P03

1000 Ljubljana

Slovenia

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Event description
DataScience@UL-FRI Workshop

About this Event

Summary

This workshop is primarily aimed at programmers (academics, professionals, or students) that know some machine learning and want to learn the basics of time series forecasting. Time series forecasting includes making accurate predictions about the future and is an important area of machine learning that is often neglected. We will focus on how to make predictions on both univariate and multivariate time series problems using standard tools in the Python data science ecosystem.

Syllabus:

  • Data preparation: load and explore time series data
  • Time series components
  • Evaluation of time series models
  • Moving average smoothing and autoregression models
  • ARIMA models for univariate time series forecasting
  • Multivariate time series forecasting
  • Deep learning for time series forecasting

Instructors

Matej Guid is an assistant professor at the Faculty of Computer and Information Science, University of Ljubljana. He has extensive experience as a chief scientist, researcher, mentor, and lecturer from the fields of Artificial Intelligence and Data Science.

Attendee equipment prerequisites

It is recommended that you bring your laptops with you, as we will try to be as interactive as possible. Please download and install Anaconda from https://www.anaconda.com/distribution/ (choose Python 3.7). You will also need Keras, Tensorflow, and Scikit-Learn.

Access to all the materials, parts of programming code, and datasets will be provided to participants a couple of days before the workshop.

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Date and Time

Location

Faculty of Computer and Information Science

113 Večna pot

Lecture Room P03

1000 Ljubljana

Slovenia

View Map

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