Naslov
Istraživanje utjecaja alternativnih podataka na predviđanje volatilnosti cijena valute Bitcoin
(Exploring the Impact of Alternative Data on Bitcoin Price Volatility Prediction)

Autori
Radović, Filip

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad, diplomski

Fakultet
Fakultet elektrotehnike i računarstva

Mjesto
Zagreb

Datum
16.09

Godina
2021

Stranica
37

Mentor
Vladimir, Klemo

Ključne riječi
Bitcoin, strojno učenje, predviđanje volatilnosti, predobrada vremenskih serija
(Bitcoin, machine learning, volatility prediction, feature importance, time series preprocessing)

Sažetak
This thesis explores and defines a new system of preprocessing data for price series prediction tasks. The system is called an event-based system and functions by sampling events from the market and aggregating data according to predefined changes in the market. The impact of many features is explored with a focus on features that are extracted from Twitter. Multiple aggregation functions are tested, and their performance is evaluated. Finally, several preprocessing methods for preprocessing time series are introduced and explained. This thesis indicates that event-based systems have potential and should be explored in more detail. It is also shown that the type of event aggregation can impact model prediction accuracy. Finally, it is shown that features from Twitter are not good indicators of price change in a high-frequency setting.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA