ARIMA vs LSTM Algorithm – A Comparative Study Based on Stock Market Prediction

Bagul, Jaikishan; Warkhade, Prajwal; Gangwal, Tanish; Mangaonkar, Nikhita

doi:10.1109/icast55766.2022.10039560

Cited by 3 publications

(1 citation statement)

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“…Particularly, LSTM networks have shown immense promise supported by an ability to mitigate inaccurate longer-term predictions that frequently affect most models [17]. Prior research found LSTMs captured price trends and changes much more accurately over traditional methods like ARIMA [18]. Bidirectional LSTM models with additional gated recurrent units have also been proposed for stock forecasting with significantly minimized deviations between predictions and ground truth [19].…”

Section: Introductionmentioning

confidence: 99%

The PSR-Transformer Nexus: A Deep Dive into Stock Time Series Forecasting

Phien,

Platos

2023

IJACSA

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Accurate stock market forecasting has remained an elusive endeavor due to the inherent complexity of financial systems dynamics. While deep neural networks have shown initial promise, robustness concerns around long-term dependencies persist. This research pioneers a synergistic fusion of nonlinear time series analysis and algorithmic advances in representation learning to enhance predictive modeling. Phase space reconstruction provides a principled way to reconstruct multidimensional phase spaces from single variable measurements, elucidating dynamical evolution. Transformer networks with self-attention have recently propelled state-of-the-art results in sequence modeling tasks. This paper introduces PSR-Transformer Networks specifically tailored for stock forecasting by feeding PSR interpreted constructs to transformer encoders. Extensive empirical evaluation on 20 years of historical equities data demonstrates significant accuracy improvements along with enhanced robustness against LSTM, CNN-LSTM and Transformer models. The proposed interdisciplinary fusion establishes new performance benchmarks on modeling financial time series, validating synergies between domain-specific reconstruction and cutting-edge deep learning.

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Section: Introductionmentioning

confidence: 99%