ARIMA for short-term and LSTM for long-term in daily Bitcoin price prediction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63570219" target="_blank" >RIV/70883521:28140/23:63570219 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-031-23492-7_12" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-23492-7_12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-23492-7_12" target="_blank" >10.1007/978-3-031-23492-7_12</a>

Result language

angličtina

Original language name

ARIMA for short-term and LSTM for long-term in daily Bitcoin price prediction

Original language description

The goal of this paper is the insight into the forecasting of Bitcoin price using machine learning models like AutoRegressive Integrated Moving Average (ARIMA), Support vector machines (SVM), hybrid ARIMA-SVM, and Long short-term memory (LSTM). Depending on the different types of data and the period, various models are used for prediction. A single model may be the best fit in the short term but may not be the best in long-term series data. Thus, using only a single model may not be suitable for forecasting time series data that depends on data sampling length and prediction time, and the type of specific applications. As a result, the ARIMA model produces better error results with a short prediction period or a small data set. In contrast, the Hybrid ARIMA-SVM model will help improve the performance of the ARIMA model when predicting over a long period, specifically 7 and 30 days for Bitcoin price prediction used in this research paper. The paper aims to compare traditional models such as the ARIMA, the Hybrid ARIMA-SVM, and deep learning models such as LSTM on a specific cryptocurrency prediction task using different scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2022, PT I

ISBN

—

ISSN

2945-9133

e-ISSN

1611-3349

Number of pages

13

Pages from-to

131-143

Publisher name

Springer

Place of publication

Cham

Event location

Zakopane

Event date

Jun 19, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000972696000012