Nonlinear combination method of forecasters applied to PM time series

Neto, Paulo S. G. de Mattos; Cavalcanti, George D. C.; Madeiro, Francisco

doi:10.1016/j.patrec.2017.06.008

Cited by 40 publications

(51 citation statements)

References 29 publications

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“…Khashei and Bijari [37] and Zhang [32] employed linear models to combine with neural network model, by using linear model to identify and magnify the linear structure of the data and then using neural network to model the preprocessed data in order to improve the prediction accuracy. Some works [32,[37][38][39][40] considered the importance of the residual series of time series data and combined the time series forecasting results to improve the performances of the hybrid model. After referring these works and some idea of online sequential algorithms, a novel hybrid forecasting model is proposed.…”

Section: Hybrid Forecasting Modelmentioning

confidence: 99%

A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting

Zhai

Tian

Cui

et al. 2020

Journal of Advanced Transportation

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For the increasing travel demands and public transport problems, dynamically adjusting timetable or bus scheduling is necessary based on accurate real-time passenger flow forecasting. In order to get more accurate passenger flow in future, this paper proposes a novel hierarchical hybrid model based on time series model, deep belief networks (DBNs), and improved incremental extreme learning machine (Im-ELM) to forecast short-term passenger flow. The proposed model is named HTSDBNE with two modelling steps. First, referring the idea of parallelization, the hybrid model, constructed by time series model, DBN, and Im-ELM, is used to forecast short-term passenger flow in different time scales hierarchically and parallel. Second, Im-ELM is utilized to analyse the relationship of forecasting results from the first step, and the weighted outputs of Im-ELM are as the final forecasting results. Comparing with single forecasting models and typical hybrid forecasting models, the testing results indicate that HTSDBNE has better performances. The mean absolute percent error of prediction results is around 10% and fully meets the application requirements of bus operation enterprise.

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Section: Hybrid Forecasting Modelmentioning

confidence: 99%

A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting

Zhai

Tian

Cui

et al. 2020

Journal of Advanced Transportation

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“…However, to achieve accurate forecasts, diversity among models must be ensured. In contrast, hybrid systems that use residual modeling are based on the combination of models generated from the time series and residual series [63]. It differs from the ensemble strategy because the time series and the residual series are distinct data sets and may present different patterns.…”

mentioning

confidence: 99%

“…for wind speed forecasting also have adopted the linear relationship supposition [3], [12], [16], [64], [65], [69]- [76]. However, the literature of hybrid systems reports that the final prediction can deteriorate, since the real relation between the linear and nonlinear forecasts is unknown [50], [63], [78]. Thus, a simple summation of the nonlinear estimates with the time series forecast may worsen the performance of the linear models, compromising the accuracy of the system.…”

mentioning

confidence: 99%

“…Alternatively, other works [50], [63], [78] have proposed another combination strategy based on the nonlinear relation between forecasts. In those studies, a trainable AI nonlinear model is employed in order to find a way of combining that is more suitable than a simple summation.…”

mentioning

confidence: 99%

“…In those studies, a trainable AI nonlinear model is employed in order to find a way of combining that is more suitable than a simple summation. Thus, a training step is performed, aiming to model the underlying relationship of the linear and nonlinear patterns [50], [63], [78]. Several combination strategies were employed in the literature.…”

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confidence: 99%

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A Hybrid Nonlinear Combination System for Monthly Wind Speed Forecasting

et al. 2020

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Wind speed is one of the primary renewable sources for clean power. However, it is intermittent, presents nonlinear patterns, and has nonstationary behavior. Thus, the development of accurate approaches for its forecasting is a challenge in wind power generation engineering. Hybrid systems that combine linear statistical and Artificial Intelligence (AI) forecasters have been highlighted in the literature due to their accuracy. Those systems aim to overcome the limitations of the single linear and AI models. In the literature about wind speed, these hybrid systems combine linear and nonlinear forecasts using a simple sum. However, the most suitable function for combining linear and nonlinear forecasts is unknown and the linear relationship assumption can degenerate or underestimate the performance of the whole system. Thus, properly combining the forecasts of linear and nonlinear models is an open question and its determination is a challenge. This paper proposes a hybrid system for monthly wind speed forecasting that uses a nonlinear combination of the linear and nonlinear models. A data-driven intelligent model is used to search for the most suitable combination, aiming to maximize the performance of the system. An evaluation has been carried out using the monthly data from three wind speed stations in northeast Brazil, evaluated with two traditional metrics. The assessment is performed for two scenarios: with and without exogenous variables. The results show that the proposed hybrid system attains an accuracy superior to other hybrid systems and single linear and AI models.

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Multistep energy consumption forecasting by metaheuristic optimization of time‐series analysis and machine learning

Chou

Truong

2020

Int J Energy Res

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Summary This study aims to develop a novel forecasting system that optimizes linear time‐series with nonlinear machine learning models to identify the historical pattern of regional energy consumption. The linear time‐series model, Seasonal AutoRegressive Integrated Moving Average (SARIMA), was applied to simulate the linear component, while the least squares support vector regression (LSSVR) model was used to capture the nonlinear component of time series energy data and combine the linear and nonlinear components. Several optimization algorithms were investigated using high‐dimension mathematical benchmark functions to compare their outcomes for applying in the proposed forecasting system. Wilcoxon rank‐sum test and convergence graphs for Jellyfish Search (JS) and other parameter‐less algorithms (ie, Teaching‐Learning‐Based Optimization (TLBO) and Symbiotic Organisms Search, SOS algorithms) indicate that the JS optimizer finds the best global solutions in mathematical tests. Then, three real energy time‐series datasets from a regional transmission organization that coordinates the movement of wholesale electricity were used to evaluate various forecasting methods. The analytical results confirm that the proposed system, JS‐LSSVR(SARIMA, LSSVR), can predict multi‐step ahead time series energy consumption with higher accuracy than the linear model (ie, SARIMA), nonlinear model (ie, LSSVR), hybrid model (ie, JS‐LSSVR), hybrid systems (ie, TLBO‐LSSVR(SARIMA, LSSVR) and TLBO‐LSSVR(SARIMA, LSSVR)), and prior studies. Numerical experiments show that the JS‐LSSVR(SARIMA, LSSVR) system can forecast energy consumption 1 week ahead efficiently (from 9.8 to 21.4 seconds on average). Notably, the proposed technique only requires 4 inputs vs 7 to 32 inputs of other models in the literature. Thus, a power company can apply the proposed system to forecast energy consumption to efficiently dispatch regional energy capacity and keep the electricity supply and demand in balance for residential buildings in sustainable cities.

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Nonlinear combination method of forecasters applied to PM time series

Cited by 40 publications

References 29 publications

A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting

A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting

A Hybrid Nonlinear Combination System for Monthly Wind Speed Forecasting

Multistep energy consumption forecasting by metaheuristic optimization of time‐series analysis and machine learning

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