Data on forecasting energy prices using machine learning

Herrera, Gabriel Paes; Constantino, Michel; Tabak, Benjamin Miranda; Pistori, Hemerson; Su, Jen‐Je; Naranpanawa, Athula

doi:10.1016/j.dib.2019.104122

Cited by 22 publications

(7 citation statements)

References 4 publications

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“…In attempt to compare performances, we evaluate other 3 models: Random Forest (RF), Support Vector Machine (SVM) with two kernels: Linear (SVML) and Radial Basis Function (SVMR), which are considered as suggested techniques for this kind of problem [14,15]. Both are considered machine learning techniques [14,53]. RF is based on a collection of decision trees, in which classifies each instance by majority vote while SVM builds a hyperplane in attempt to optimize the division between classes.…”

Section: Proposed Model and Benchmarksmentioning

confidence: 99%

Forecasting Brazilian Ethanol Spot Prices Using LSTM

et al. 2021

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Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios.

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Section: Proposed Model and Benchmarksmentioning

confidence: 99%

Forecasting Brazilian Ethanol Spot Prices Using LSTM

et al. 2021

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“…Significant dataset is divided into two groups as training and testing according to previous studies [23]- [25]. Dataset of 405 individuals is spilt into 8:2 ratios.…”

Section: ) Data Splitmentioning

confidence: 99%

Predicting Ayurveda-Based Constituent Balancing in Human Body Using Machine Learning Methods

Madaan

Goyal

2020

IEEE Access

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Human Body constitution (prakriti) defines what is in harmony with human nature and what will cause to move out of balance and experience illness. Tridosha defines the three basic energies or principles that determine the function of our body on the physical and emotional levels. The three energies are known as VATT, PITT and KAPH. Each individual has a unique balance of all three of these energies. Some people will be predominant in one, while others will be a mixture of two or more. Ayurveda-dosha studies have been used for a long time, but the quantitative reliability measurement of these diagnostic methods still lags behind. A careful and appropriate analysis leads to an effective treatment. To collect a meaningful data set, a questionnaire with 28 different characteristics is validated by Ayurveda experts. Authors calculate Cronbach alpha of VATT-Dosha, PITT-Dosha and KAPH-Dosha as 0.94, 0.98 and 0.98, respectively to check the reliability of the questionnaire. Authors analyzed questionnaires of 807 healthy persons aged 20-60 years and found 62.1% men and 37.9% women. The class imbalance problem is resolved with oversampling and the equally distributed data set of randomly selected 405 persons is used for the actual experiment. Using computer algorithms, we randomly divide the data set (8:2) into a training set of 324 persons and a test data set of 81 persons. Model is trained using traditional machine learning techniques for classification analysis as Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB) and Decision Tree (DT). System is also implemented using ensemble of several machine learning methods for constitution recognition. Evaluation measures of classification such as root mean square error (RMSE), precision, recall, F-score, and accuracy is calculated and analyzed. On analyzing the results authors find that the data is best trained and tested with CatBoost, which is tuned with hyper parameters and achieves 0.96 precision, 0.95 recall, 0.95 F-score and 0.95 accuracy rate. The experimental result shows that the proposed model based on ensemble learning methods clearly surpasses conventional methods. The results conclude that advances in boosting algorithms could give machine learning a leading future. INDEX TERMS Ayurveda, human body constituents, hyper parameter tuning, KAPH, optimized training model, PITT, VATT.

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“…There is an extensive research work on the oil price volatility since the time of oil crises (1970 s) during past two decades. With the origin of oil crises in the 1970 s and the attention received from researchers on the works of Hamilton (1983) oil price increases and productivity positive related conclusions are on debate with different views on the relationship based on different factors and also widely accepted by many researchers along the most influenced work by Herrera et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Pass-through Effects of Oil Prices on LATAM Emerging Stocks before and during COVID-19: An Evidence from a Wavelet -VAR Analysis

Gaytán

Rafiuddin

Sisodia

et al. 2023

IJEEP

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Vector Auto regression model (VAR) a time -varying parameter is applied to study the effect of oil price shocks on the returns of stocks in the LATAM (Latin American) markets. Coherent Wavelet analysis highlights possibilities of connectedness of the oil price and LATAM stock markets through the presence of different patterns in a time series. The structural demand shocks standard deviations during the COVID-19 era remain high and the pass-through effects on stock returns due to oil prices differ for different time frames. The fundamental linkages are demonstrated due to oil market specific demand. The main motive of the research work is to identify the influence of oil price on stocks and identify the fundamental source of contagion. A random effects model is applied to the panel data of LATAM markets with the Global stock market index, MSCI (Morgan Stanley Capital International World Index), domestic money market rates and currency exchange rates during the period of study, 15 March 2019 to 31 July 2021 with 684 observations of controlled non-observed characteristics from individual country. The findings of this research recommend the pass-through effect of the oil prices on the stock market returns are based on time frequency. The contribution of this paper helps the policy makers to restore the confidence amongst the investors in the stock markets and strategies to be adopted by the investors to mitigate the risk by ideal portfolio management.

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Data on forecasting energy prices using machine learning

Cited by 22 publications

References 4 publications

Forecasting Brazilian Ethanol Spot Prices Using LSTM

Forecasting Brazilian Ethanol Spot Prices Using LSTM

Predicting Ayurveda-Based Constituent Balancing in Human Body Using Machine Learning Methods

Pass-through Effects of Oil Prices on LATAM Emerging Stocks before and during COVID-19: An Evidence from a Wavelet -VAR Analysis

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