A note on minimizing absolute percentage error in combined forecasts

Lam, Kim Fung; Mui, H.W.; Yuen, Hak-Keung

doi:10.1016/s0305-0548(00)00026-5

Cited by 36 publications

(19 citation statements)

References 13 publications

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“…To systematically examine the performance of different models by statistical error measures and characteristics of precipitation process error, we include results with two widely used prediction error metrics: Symmetric Mean Absolute Percentage Error (SMAPE) [41] and Root Mean Square Error (RMSE) [42].…”

Section: Evaluating the Performancementioning

confidence: 99%

Prediction of Precipitation Based on Recurrent Neural Networks in Jingdezhen, Jiangxi Province, China

et al. 2020

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Precipitation is a critical input for hydrologic simulation and prediction, and is widely used for agriculture, water resources management, and prediction of flood and drought, among other activities. Traditional precipitation prediction researches often established one or more probability models of historical data based on the statistical prediction methods and machine learning techniques. However, few studies have been attempted deep learning methods such as the state-of-the-art for Recurrent Neural Networks (RNNs) networks in meteorological sequence time series predictions. We deployed Long Short-Term Memory (LSTM) network models for predicting the precipitation based on meteorological data from 2008 to 2018 in Jingdezhen City. After identifying the correlation between meteorological variables and the precipitation, nine significant input variables were selected to construct the LSTM model. Then, the selected meteorological variables were refined by the relative importance of input variables to reconstruct the LSTM model. Finally, the LSTM model with final selected input variables is used to predict the precipitation and the performance is compared with other classical statistical algorithms and the machine learning algorithms. The experimental results show that the LSTM is suitable for precipitation prediction. The RNN models, combined with meteorological variables, could predict the precipitation accurately in Jingdezhen City and provide sufficient time to prepare strategies against potential related disasters.

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Section: Evaluating the Performancementioning

confidence: 99%

Prediction of Precipitation Based on Recurrent Neural Networks in Jingdezhen, Jiangxi Province, China

et al. 2020

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“…To obtain the weights to build the combination of forecasts that minimises the MAPE, we proceed by extending the approach suggested by Lam et al (2001) …”

Section: Minimising the Mape In Combined Forecastsmentioning

confidence: 99%

“…Moreover, in this paper, the computational experiments performed in García-Martos et al (2007, 2012 are extended twofold: first, we do not impose the constraint about fitting the same model for every hour, which could give a sub-optimal solution in terms of prediction accuracy. Second, for those hours in which several models are not significantly different in terms of their accuracy, an optimal combination of forecasts (Bates and Granger, 1969) based on minimising the out-of-sample Mean Absolute Percentage Error (MAPE) is built by extending the ideas in Lam et al (2001). The technique of combining forecasts has been used by several authors for computing forecasts of interest in the energy sector, but these forecasts mainly involve load and wind power production (see Taylor and Majithia, 2000;Sánchez, 2006 and2008).…”

Section: Introductionmentioning

confidence: 99%

Electricity price forecasting accounting for renewable energies: optimal combined forecasts

García‐Martos

Caro

Sánchez

2015

Journal of the Operational Research Society

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Electricity price forecasting is an interesting problem for all the agents involved in electricity market operation. For instance, every profit maximisation strategy is based on the computation of accurate one-day-ahead forecasts, which is why electricity price forecasting has been a growing field of research in recent years. In addition, the increasing concern about environmental issues has led to a high penetration of renewable energies, particularly wind. In some European countries such as Spain, Germany and Denmark, renewable energy is having a deep impact on the local power markets. In this paper, we propose an optimal model from the perspective of forecasting accuracy, and it consists of a combination of several univariate and multivariate time series methods that account for the amount of energy produced with clean energies, particularly wind and hydro, which are the most relevant renewable energy sources in the Iberian Market. This market is used to illustrate the proposed methodology, as it is one of those markets in which wind power production is more relevant in terms of its percentage of the total demand, but of course our method can be applied to any other liberalised power market. As far as our contribution is concerned, first, the methodology proposed by García-Martos et al (2007 and2012) is generalised twofold: we allow the incorporation of wind power production and hydro reservoirs, and we do not impose the restriction of using the same model for 24 h. A computational experiment and a Design of Experiments (DOE) are performed for this purpose. Then, for those hours in which there are two or more models without statistically significant differences in terms of their forecasting accuracy, a combination of forecasts is proposed by weighting the best models (according to the DOE) and minimising the Mean Absolute Percentage Error (MAPE). The MAPE is the most popular accuracy metric for comparing electricity price forecasting models. We construct the combination of forecasts by solving several nonlinear optimisation problems that allow computation of the optimal weights for building the combination of forecasts. The results are obtained by a large computational experiment that entails calculating out-of-sample forecasts for every hour in every day in the period from January 2007 to December 2009. In addition, to reinforce the value of our methodology, we compare our results with those that appear in recent published works in the field. This comparison shows the superiority of our methodology in terms of forecasting accuracy.

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“…Kolassa (2011) combined exponential smoothing forecasts using Akaike weights to perform better than forecasts selected using information criteria, in terms of accuracy and interval coverage. Lam et al (2001) proposed two new approaches of combination 647 PSO-BP neural network forecasts, which minimized absolute percentage error by use of minimizing mean absolute percentage error (MAPE) or minimizing the maximum absolute percentage error. Jeong and Kim (2009) provided a guideline for combining methods, in which bias and non-stationary of the errors in individual forecasts, the ration of the error variance of any two forecasts and cross-correlation among the forecasts should be considered.…”

Section: Introductionmentioning

confidence: 99%

Adaptive combination forecasting model for China’s logistics freight volume based on an improved PSO-BP neural network

Zhou

Tao

2015

Kybernetes

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Purpose -To accurately forecast logistics freight volume plays a vital part in rational planning formulation for a country. The purpose of this paper is to contribute to developing a novel combination forecasting model to predict China's logistics freight volume, in which an improved PSO-BP neural network is proposed to determine the combination weights. Design/methodology/approach -Since BP neural network has the ability of learning, storing, and recalling information that given by individual forecasting models, it is effective in determining the combination weights of combination forecasting model. First, an improved PSO based on simulated annealing method and space-time adjustment strategy (SAPSO) is proposed to solve out the connection weights of BP neural network, which overcomes the problems of local optimum traps, low precision and poor convergence during BP neural network training process. Then, a novel combination forecast model based on SAPSO-BP neural network is established. Findings -Simulation tests prove that the proposed SAPSO has better convergence performance and more stability. At the same time, combination forecasting models based on three types of BP neural networks are developed, which rank as SAPSO-BP, PSO-BP and BP in accordance with mean absolute percentage error (MAPE) and convergent speed. Also the proposed combination model based on SAPSO-BP shows its superiority, compared with some other combination weight assignment methods. Originality/value -SAPSO-BP neural network is an original contribution to the combination weight assignment methods of combination forecasting model, which has better convergence performance and more stability.

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A note on minimizing absolute percentage error in combined forecasts

Cited by 36 publications

References 13 publications

Prediction of Precipitation Based on Recurrent Neural Networks in Jingdezhen, Jiangxi Province, China

Prediction of Precipitation Based on Recurrent Neural Networks in Jingdezhen, Jiangxi Province, China

Electricity price forecasting accounting for renewable energies: optimal combined forecasts

Adaptive combination forecasting model for China’s logistics freight volume based on an improved PSO-BP neural network

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