Deep learning based regime-switching models of energy commodity prices

Mari, Carlo

doi:10.1007/s12667-022-00515-6

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Brusaferri et al proposed a novel methodology utilizing Bayesian DL techniques for probabilistic energy price forecasting [442]. Mari et al discussed a DL-based approach to model the complex dynamics of commodity prices observed in real markets [443]. Scholz et al presented and analyzed a novel approach for predicting the energy price in the continuous intra-day market at the European power exchange spot [444].…”

Section: Modeling the Energy Market: From Power Grid Data To Energy P...mentioning

confidence: 99%

Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey

et al. 2023

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To face the impact of climate change in all dimensions of our society in the near future, the European Union (EU) has established an ambitious target. Until 2050, the share of renewable power shall increase up to 75% of all power injected into nowadays’ power grids. While being clean and having become significantly cheaper, renewable energy sources (RES) still present an important disadvantage compared to conventional sources. They show strong fluctuations, which introduce significant uncertainties when predicting the global power outcome and confound the causes and mechanisms underlying the phenomena in the grid, such as blackouts, extreme events, and amplitude death. To properly understand the nature of these fluctuations and model them is one of the key challenges in future energy research worldwide. This review collects some of the most important and recent approaches to model and assess the behavior of power grids driven by renewable energy sources. The goal of this survey is to draw a map to facilitate the different stakeholders and power grid researchers to navigate through some of the most recent advances in this field. We present some of the main research questions underlying power grid functioning and monitoring, as well as the main modeling approaches. These models can be classified as AI- or mathematically inspired models and include dynamical systems, Bayesian inference, stochastic differential equations, machine learning methods, deep learning, reinforcement learning, and reservoir computing. The content is aimed at the broad audience potentially interested in this topic, including academic researchers, engineers, public policy, and decision-makers. Additionally, we also provide an overview of the main repositories and open sources of power grid data and related data sets, including wind speed measurements and other geophysical data.

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Section: Modeling the Energy Market: From Power Grid Data To Energy P...mentioning

confidence: 99%

Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey

et al. 2023

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A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis

Khattak,

Shafi,

Khan

et al. 2023

IEEE Access

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Artificial intelligence (AI)-based models have emerged as powerful tools in financial markets, capable of reducing investment risks and aiding in selecting highly profitable stocks by achieving precise predictions. This holds immense value for investors, as it empowers them to make data-driven decisions. Identifying current and future trends in multi-class forecasting techniques employed within financial markets, particularly profitability analysis as an evaluation metric is important. The review focuses on examining studies conducted between 2018 and 2023, sourced from three prominent academic databases. A meticulous three-stage approach was em-ployed, encompassing the systematic planning, conduct, and analysis of the selected studies. Specifically, the analysis emphasizes technical assessment, profitability analysis, hybrid modeling, and the type of re-sults generated by models. Articles were shortlisted based on inclusion and exclusion criteria, while a rigorous quality assessment through ten quality criteria questions, utilizing a Likert-type scale was employed to ensure methodological robustness. We observed that ensemble and hybrid models with long short-term memory (LSTM) and support vector machines (SVM) are being more adopted for financial trends and price prediction. Moreover, hybrid models employing AI algorithms for feature engineering have great potential at par with ensemble techniques. Most studies only employ performance metrics and lack utilization of profitability metrics or investment or trading strategy (simulated or real-time). Similarly, research on multi-class or output is severely lacking in financial forecasting and can be a good avenue for future research. INDEX TERMS Artificial intelligence; financial forecasting; deep learning; stock market analysis; convolution neural network; cryptocurrency I. INTRODUCTIONF ORECASTING asset prices in financial markets pose significant challenges due to the intricate interplay of various micro and macroeconomic attributes that influence price formation including political events, news, and company financial statements. These multifaceted factors contribute to the non-linearity and non-stationarity observed in the market, thereby intensifying the complexity of the proposed task. Consequently, market analysis is conducted to study these influences, aiming to predict future market trends and support decision-making based on market behavior.There are various types of financial markets such as the stock market, foreign exchange, commodities, and cryptocurrency. Stock markets offer a diverse range of instruments, such as stocks, bonds, and derivatives, that can be used for both long-term and short-term capital generation [1].

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