Name: Workshop on Time Series Analysis
Start: 2019-02-12T19:00:00Z
End: 2019-02-12T21:00:00Z
Location: Chaplins Bar

Venue Capacity: 30 people

The capacity of the venue is about 30 people but because of no-shows, we have a policy of first-come, first-served on the night in the event of the room filling up. Managing no-shows is a problem all Meetups have to deal with and this is the fairest method we can think of. While we have never been in a position to turn anyone away for capacity reasons, it is always a possibility, so please arrive early to avoid disappointment on the night. We have the upstairs room booked from 1900-2100 so if the room is in use till that time, we will gather in the bar below beforehand.

https://github.com/kaybenleroll/data_workshops

This workshop is contained in the ws_timeseries_201901/ folder

This is the second workshop in a multi-part series on time-series analysis. We assume no prior knowledge and try to make each workshop as standalone as possible.

This workshop continues the discussion on basic manipulation and exploration of time series. We introduce the concept of the decomposition of a time series into trend, seasonal and random components, discussing some standard methods used to perform this decomposition.

While this workshop series will focus on the theoretical underpinnings of the topic and I try to make this as language-neutral as possible, the technical work will be done in R, as it has a large range of packages useful for time series modelling, and in particular I will discuss the use of the package 'tidyquant'. That said, people with an interest in the topic and without a knowledge of R are welcome as it is always good to get different perspectives from people.

*Please bring fully charged laptops and your own internet connectivity - tethering off a phone should be fine. Further details of software and libraries used will be provided closer to the time.*

Mick Cooney

Dublin Data Science

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Workshop on Time Series Analysis

Chaplins Bar

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Dublin Data Science

Workshop on Time Series Analysis

Dublin Data Science

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