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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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Stephen Haben, Julien Caudron and Jake Verma
The energy sector is moving towards a low-carbon, decentralised, and smarter network. The increased uptake of distributed renewable energy and cheaper storage devices provide opportunities for new local energy markets. These local energy markets will req...
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Marin Cerjan, Ana Petricic and Marko Delimar
In competitive power markets, electric utilities, power producers, and traders are exposed to increased risks caused by electricity price volatility. The growing reliance on renewable sources and their dependence on weather, nuclear uncertainty, market c...
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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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Sajjad Khan, Shahzad Aslam, Iqra Mustafa and Sheraz Aslam
Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators...
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Masahiro Suzuki, Hiroki Sakaji, Kiyoshi Izumi, Hiroyasu Matsushima and Yasushi Ishikawa
This paper proposes and analyzes a methodology of forecasting movements of the analysts? net income estimates and those of stock prices. We achieve this by applying natural language processing and neural networks in the context of analyst reports. In the...
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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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Daniel Manfre Jaimes, Manuel Zamudio López, Hamidreza Zareipour and Mike Quashie
This paper proposes a new hybrid model to forecast electricity market prices up to four days ahead. The components of the proposed model are combined in two dimensions. First, on the ?vertical? dimension, long short-term memory (LSTM) neural networks and...
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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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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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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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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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Denis E. Baskan, Daniel Meyer, Sebastian Mieck, Leonhard Faubel, Benjamin Klöpper, Nika Strem, Johannes A. Wagner and Jan J. Koltermann
In recent years, energy prices have become increasingly volatile, making it more challenging to predict them accurately. This uncertain market trend behavior makes it harder for market participants, e.g., power plant dispatchers, to make reliable decisio...
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Loris Belcastro, Domenico Carbone, Cristian Cosentino, Fabrizio Marozzo and Paolo Trunfio
Since the advent of Bitcoin, the cryptocurrency landscape has seen the emergence of several virtual currencies that have quickly established their presence in the global market. The dynamics of this market, influenced by a multitude of factors that are d...
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Khalid Alkhatib, Huthaifa Khazaleh, Hamzah Ali Alkhazaleh, Anas Ratib Alsoud and Laith Abualigah
Stock price prediction is a significant research field due to its importance in terms of benefits for individuals, corporations, and governments. This research explores the application of the new approach to predict the adjusted closing price of a specif...
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Emmanuel Senyo Fianu
Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing methodologies cannot accommodate their unique properties. From this viewpoint, we propose an extended mode decomposition method useful for the time-frequen...
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Hassan Hamie, Anis Hoayek and Hans Auer
The question of whether the liberalization of the gas industry has led to less concentrated markets has attracted much interest among the scientific community. Classical mathematical regression tools, statistical tests, and optimization equilibrium probl...
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Polash Dey, Emam Hossain, Md. Ishtiaque Hossain, Mohammed Armanuzzaman Chowdhury, Md. Shariful Alam, Mohammad Shahadat Hossain and Karl Andersson
Investors in the stock market have always been in search of novel and unique techniques so that they can successfully predict stock price movement and make a big profit. However, investors continue to look for improved and new techniques to beat the mark...
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Efthymios Stathakis, Theophilos Papadimitriou, Periklis Gogas
Pág. 65 - 87
Electricity markets are considered to be the most volatile amongst commodity markets. The non-storability of electricity and the need for instantaneous balancing of demand and supply can often cause extreme short-lived fluctuations in electricity prices....
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Efthymios Stathakis, Theophilos Papadimitriou, Periklis Gogas
Electricity markets are considered to be the most volatile amongst commodity markets. The non-storability of electricity and the need for instantaneous balancing of demand and supply can often cause extreme short-lived fluctuations in electricity prices....
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