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Manuel Zamudio López, Hamidreza Zareipour and Mike Quashie
This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Numerical definitions for price spikes are derived from economic and statistical thresholds. The predictive task employ...
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Krzysztof Drachal and Michal Pawlowski
This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities? prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed. The conventional approa...
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C. Tamilselvi, Md Yeasin, Ranjit Kumar Paul and Amrit Kumar Paul
Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of pr...
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Ilia Zaznov, Julian Martin Kunkel, Atta Badii and Alfonso Dufour
This paper introduces a novel deep learning approach for intraday stock price direction prediction, motivated by the need for more accurate models to enable profitable algorithmic trading. The key problems addressed are effectively modelling complex limi...
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Anuwat Boonprasope and Korrakot Yaibuathet Tippayawong
Following the COVID-19 pandemic, the healthcare sector has emerged as a resilient and profitable domain amidst market fluctuations. Consequently, investing in healthcare securities, particularly through mutual funds, has gained traction. Existing researc...
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Marius-Ionu? Gordan, Cosmin Alin Popescu, Jenica Calina, Tabita Cornelia Adamov, Camelia Maria Manescu and Tiberiu Iancu
Seasonal variations in the tourism industry consist of alternating patterns of overuse and underuse of touristic potential and resources, which correspond to overexertion in the peak periods and to reduced income levels in the trough periods. We analyze ...
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Tiago Silveira Gontijo,Marcelo Azevedo Costa,Rafael Isaac dos Santos,Rodrigo Barbosa de Santis
Developing forecasting models is a difficult task. Particularly concerning electricity prices, accurately predicting their forthcoming values makes it possible to minimize planning risks. This fact becomes even more relevant in the current geopolitical s...
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Markus Frohmann, Manuel Karner, Said Khudoyan, Robert Wagner and Markus Schedl
Recently, various methods to predict the future price of financial assets have emerged. One promising approach is to combine the historic price with sentiment scores derived via sentiment analysis techniques. In this article, we focus on predicting the f...
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Sumathi Kumaraswamy, Yomna Abdulla and Shrikant Krupasindhu Panigrahi
Recurrent stock market fall and rise sequel by COVID-19, rising global inflation, increase in Fed interest rates, the unprecedented meltdown of technology stocks, fear of trade wars, tightening of governments? fiscal policies call for a new trend in inte...
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L. Thanga Mariappan, J. Arun Pandian, V. Dhilip Kumar, Oana Geman, Iuliana Chiuchisan and Carmen Nastase
Cryptocurrency has emerged as a well-known significant component with both economic and financial potential in recent years. Unfortunately, Bitcoin acquisition is not simple, due to uneven business and significant rate fluctuations. Traditional approache...
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