Forecasting natural gas consumption using Bagging and modified regularization techniques

Meira, Erick; Oliveira, Fernando Luiz Cyrino; Menezes, Lilian M. de

doi:10.1016/j.eneco.2021.105760

Cited by 30 publications

(13 citation statements)

References 66 publications

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“…Meira et al (2021) considered the use of Seasonal Trend Decomposition using Loess (STL) (Cleveland et al, 1990) to isolate key components of electricity supply time series before using exponential smoothing models, thus improving forecasting performance over several benchmarks. A similar process was adopted by Meira et al (2022) to decompose key components of natural gas consumption time series. Athoillah et al (2021) applied Support Vector Regression (SVR) to predict rainfall based on reconstructed series from an SSA decomposition, which was assumed to be free from noisy elements.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting commodity prices in Brazil through hybrid SSA-complex seasonality models

Palazzi

Maçaira

Meira

et al. 2023

Prod.

Self Cite

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Paper aims: To predict monthly corn, soybean, and sugar spot prices in Brazil using hybrid forecasting techniques.Originality: This study combines the Singular Spectrum Analysis with different forecasting methods.Research method: This paper presents a set of hybrid forecasting approaches combining Singular Spectrum Analysis (SSA) with different univariate time series methods, ranging from complex seasonality methods to machine learning and autoregressive models to predict monthly corn, soybean, and sugar spot prices in Brazil. We carry out a range of out-ofsample forecasting experiments and use a comprehensive set of forecast evaluation metrics. We contrast the performance of the proposed approaches with that of a range of benchmark models. Main findings:The results show that the proposed hybrid models present better performances, with the hybrid SSA-neural network approach providing the most competitive results in our sample.Implications for theory and practice: Forecasting agricultural prices is of paramount importance to assist producers, farmers, and the industry in decision-making processes.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting commodity prices in Brazil through hybrid SSA-complex seasonality models

Palazzi

Maçaira

Meira

et al. 2023

Prod.

Self Cite

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“…Meira et al combined bootstrap aggregation, univariate time series estimation methods and modified regularization routines in their study. They introduced a new type of bagging that uses maximum entropy bootstrap and a modified regularization routine that keeps the data generation process in the community [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of hybrid and non-hybrid models in short-term predictions on time series in the R development environment

Pala

Ünlük

2022

DÜMF MD

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Because many time series usually contain both linear and nonlinear components, a single linear or nonlinear model may be insufficient for modeling and predicting time series. Therefore, estimation results are tried to be improved by using collaborative models in time series short-term prediction processes. In this study, the performances of both stand-alone models and models whose different combinations can be used in a hybrid environment are compared. The mean absolute percentage error (MAPE) metric values obtained from two different categories were evaluated. In addition, the estimation performances of three different approaches such as equi-weighted (EW), variable-weighted (VW) and cross-validation-weighted (CVW) for hybrid operation were also compared. The findings on the container throughput forecast of the Airpassengers dataset reveal that the hybrid model's forecasts outperform the non-combined model.

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“…Many authors support proposals for hybrid models, in which several techniques are combined, however, a complete comparison to determine if the approach is the most adequate is usually not performed. In this paper, the models long short-term memory (LSTM) [27], group method of data handling (GMDH) [28], adaptive neuro fuzzy inference system (ANFIS) [29], bagging [30], boosting [31], random subspace [32], and stacking ensemble learning models [33] are compared. After defining the structure of each model that has the best performance for the used data set, the hyperparameters of the models and the use of Wavelet transform are evaluated, which has wide application for time series [34].…”

Section: Introductionmentioning

confidence: 99%

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

Neto¹,

Stefenon²,

Meyer³

et al. 2022

Preprint

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To improve the monitoring of the electrical power grid it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface, and to evaluate the supportability of these components. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. Choosing which method to use is always a difficult task since some models may have higher computational effort. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. A review and comparison of these well-established methods for time series forecasting is performed. From the results of the best structure of the model, the hyperparameters are evaluated and the Wavelet transform is used to obtain an enhanced model.

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Forecasting natural gas consumption using Bagging and modified regularization techniques

Cited by 30 publications

References 66 publications

Forecasting commodity prices in Brazil through hybrid SSA-complex seasonality models

Forecasting commodity prices in Brazil through hybrid SSA-complex seasonality models

Comparison of hybrid and non-hybrid models in short-term predictions on time series in the R development environment

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

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