Unveiling forecasting potential: ARIMA vs. LSTM for NZX50 Index time series analysis
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Authors
Dassanayake, Wajira
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Date
2024-12-04
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Conference Contribution - Oral Presentation
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New Zealand
New Zealand Stock Exchange (NZX)
stock price analysis
prediction
Long Short-Term Memory (LSTM)
Auto-Regressive Integrated Moving Average (ARIMA)
deep-learning algorithms
computer modelling
algorithms
New Zealand Stock Exchange (NZX)
stock price analysis
prediction
Long Short-Term Memory (LSTM)
Auto-Regressive Integrated Moving Average (ARIMA)
deep-learning algorithms
computer modelling
algorithms
ANZSRC Field of Research Code (2020)
Citation
Dassanayake, W. (2024, December 2-6). Unveiling forecasting potential: ARIMA vs. LSTM for NZX50 Index time series analysis [Paper presentation]. ITP Research Symposium 2024 + OPSITARA 2024, Auckland, New Zealand
https://hdl.handle.net/10652/6769
Abstract
PURPOSE
The purpose of this presentation is to evaluate the forecasting effectiveness of the ARIMA model (a statistical approach) in comparison to the LSTM model (an advanced Machine Learning and Deep Learning technique)
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