Machine learning in energy economics and finance: A review

Ghoddusi, Hamed; Creamer, Germán G.; Rafizadeh, Nima

doi:10.1016/j.eneco.2019.05.006

Cited by 304 publications

(132 citation statements)

References 187 publications

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“…Deep Neural Networks (DNNs) have become the standard building block in numerous machine learning applications, including computer vision [10], speech recognition [2], machine translation [34], and robotic manipulation [16], achieving state-of-the-art performance on extremely difficult tasks. The widespread success of these networks has made them the prime option for deploying in sensitive domains, including but not limited to health care [25], finance [7], autonomous driving [3], and defense-related applications [23].…”

Section: Introductionmentioning

confidence: 99%

Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs

Kolouri

Saha

Pirsiavash

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

144

140

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The unprecedented success of deep neural networks in many applications has made these networks a prime target for adversarial exploitation. In this paper, we introduce a benchmark technique for detecting backdoor attacks (aka Trojan attacks) on deep convolutional neural networks (CNNs). We introduce the concept of Universal Litmus Patterns (ULPs), which enable one to reveal backdoor attacks by feeding these universal patterns to the network and analyzing the output (i.e., classifying the network as 'clean' or 'corrupted'). This detection is fast because it requires only a few forward passes through a CNN. We demonstrate the effectiveness of ULPs for detecting backdoor attacks on thousands of networks with different architectures trained on four benchmark datasets, namely the German Traffic Sign Recognition Benchmark (GTSRB), MNIST, CIFAR10, and Tiny-ImageNet. The codes and train/test models for this paper can be found here: https://umbcvision. github.io/Universal-Litmus-Patterns/.

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Section: Introductionmentioning

confidence: 99%

Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs

Kolouri

Saha

Pirsiavash

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

144

140

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“…Recently Ghoddusi et al [24] reviewed machine learning methodologies applied to energy economics and finance with particular emphasis on energy prices, demand forecasting, risk strategies, and analysis of the energy trends.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Energy Consumption in the EU Residential Sector

Bianco

Marchitto

Scarpa

et al. 2020

IJERPH

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The present paper aims to introduce a top down methodology for the forecasting of residential energy demand in four European countries, namely Germany, Italy, Spain, and Lithuania. The methodology employed to develop the estimation is based on econometric techniques. In particular, a logarithmic dynamic linear constant relationship of the consumption is proposed. Demand is estimated as a function of a set of explaining variables, namely heating degree days and gross domestic product per capita. The results confirm that the methodology can be applied to the case of Germany, Italy, and Spain, whereas it is not suitable for Lithuania. The analysis of elasticities of the demand with respect to the gross domestic product per capita shows a negative value for Germany, −0.629, and positive values for Italy, 0.837, and Spain, 0.249. The forecasting of consumption shows that Germany and Italy are more sensitive to weather conditions with respect to Spain and an increase in the demand of 8% and 9% is expected in case of cold climatic conditions.

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“…Machine learning focuses on many algorithms and techniques that allow a computer system to learn -that is, a change in the internal state of the system that will be able to adapt effectively to the environmental changes [1][2][3]. Ghoddusi, Creamer and Rafizadeh [4] state in their study that machine learning enables to create new opportunities for innovative research in the field of finance and energy economy. The authors focus on applications in areas such as demand forecasting, energy price forecasting (eg natural gas, energy and oil), business strategy, risk management, macro / energy trend analysis and data processing.…”

Section: Introductionmentioning

confidence: 99%

Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions

2020

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The USA decided to regulate the trade more by imposing tariffs on specific types of traded goods. It is therefore more interesting to find out whether the current technologies based on artificial intelligence with time series influenced by extraordinary factors such as the trade war between two powers are able to work. The objective of the contribution is to examine and subsequently equalize two time series – the USA import from the PRC and the USA export to the PRC. The dataset shows the course of the time series at monthly intervals between January 2000 and July 2019. 10,000 multilayer perceptron networks (MLP) are generated, out of which 5 with the best characteristics are retained. It has been proved that multilayer perceptron networks are a suitable tool for forecasting the development of the time series if there are no sudden fluctuations. Mutual sanctions of both states did not affect the result of machine learning forecasting.

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Machine learning in energy economics and finance: A review

Cited by 304 publications

References 187 publications

Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs

Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs

Forecasting Energy Consumption in the EU Residential Sector

Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions

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