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Prediction of Litecoin Prices using ARIMA and LSTM

Chandrasekaran, Ranjani (2019) Prediction of Litecoin Prices using ARIMA and LSTM. Masters thesis, Dublin, National College of Ireland.

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Abstract

Since its inception in 2009, cryptocurrency has attracted attention from investors and the academia as well. The significant growth in market capitalization, types and the volatility of cryptocurrencies has spurred considerable interest and studies by researchers. However, most studies are on the hugely popular bitcoin. The latest study shows that Litecoin though occupying the fourth place at 2.74% market cap as on 1st August 2019, has attracted institutional investors. This is a breakthrough, wherein for the first time, cryptocurrency was seen in equal terms with fiat currency. This significant development is attributed to Litecoin’s unique features of low cost of mining, real-time speed of transactions, backed by strong blockchain based cost-effective architecture which are capable of handling higher volumes than bitcoin. This research is aimed at the price prediction of Litecoin using machine models. Two models Auto-Regressive Integrated Moving Average (ARIMA) and Long short-term Memory (LSTM) are used for data analysis to understand the best possible model for price prediction. Data collected over a five-and-a-half-year period from 2014 to 2019 is analysed and both the models are evaluated with MAPE, ME, MAE, and RMSE performance parameters. The best results for Litecoin price prediction are achieved when LSTM model is used with a MAPE of 5.759%.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

H Social Sciences > HG Finance > Banking > E-banking
H Social Sciences > HF Commerce > Electronic Commerce > Mobile Commerce
Divisions: School of Computing > Master of Science in FinTech
Depositing User: Dan English
Date Deposited: 02 Jun 2020 12:17
Last Modified: 02 Jun 2020 12:17
URI: http://trap.ncirl.ie/id/eprint/4222

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