A hybrid ETS–ANN model for time series forecasting

Panigrahi, Sibarama; Behera, Himansu Sekhar

doi:10.1016/j.engappai.2017.07.007

Cited by 165 publications

(71 citation statements)

References 27 publications

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“…In details, the ANN-Backpropagation architecture is shown in Figure 1. The training procedures of this algorithm are reported in many investigated fields [53]- [56].…”

Section: Problem Statementmentioning

confidence: 99%

Demand Forecasting Considering Actual Peak Load Periods Using Artificial Neural Network

Yuan¹,

Afandi²,

Putranto³

2018

EECSI

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Presently, electrical energy consumption continues to increase from year to year. Therefore, a short-term load forecasting is required that electricity providers can deliver continuous electrical energy to electricity consumers. By considering the estimation of the electrical load, the scheduling plan for operation and allocation of reserves can be managed well by the supply side. This study is focused on a forecasting of electrical loads using Artificial Neural Network (ANN) method considering a backpropagation algorithm model. The advantage of this method is to forecast the electrical load in accordance with patterns of past loads that have been taught. The data used for the learning is Actual Peak Load Period (APLP) data on the 150 kV system during 2017. Results show that the best network architecture is structured for the APLP Day and Night. Moreover, the momentum setting and understanding rate are 0.85 and 0.1 for the APLP Day. In contrast, 0.9 and 0.15 belong to the APLP Night. Based on the best network architecture, the APLP day testing process generates Mean Squared Error (MSE) around 0.04 and Mean Absolute Percentage Error (MAPE) around 4.66%, while the APLP Night generates MSE in 0.16 and MAPE in 16.83%.

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“…In details, the ANN-Backpropagation architecture is shown in Figure 1. The training procedures of this algorithm are reported in many investigated fields [53]- [56].…”

Section: Problem Statementmentioning

confidence: 99%

Demand Forecasting Considering Actual Peak Load Periods Using Artificial Neural Network

Yuan¹,

Afandi²,

Putranto³

2018

EECSI

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“…Some other studies related to what we propose in this work can be seen in [3][4][5][6][7][8][9][10][11]. In the study by Liangping and Sternberg, two approaches are proposed to predict the Peak Signal-to-Noise Ratio (PSNR) in video transmissions.…”

Section: Introductionmentioning

confidence: 99%

“…In study [7], the authors propose a hybrid ARIMA and support vector machines (SVM) neural networks for forecasting stock prices. In [8], the authors propose a technique for time series forecasting where models from state space (ETS) modelling for exponential smoothing are combined with a neural network. e aim is to enable the authors to obtain different combinations of linear or nonlinear patterns in a time series more easily.…”

Section: Introductionmentioning

confidence: 99%

Application of Hybrid ARIMA and Artificial Neural Network Modelling for Electromagnetic Propagation: An Alternative to the Least Squares Method and ITU Recommendation P.1546-5 for Amazon Urbanized Cities

Lopes

Fraiha

Gomes

et al. 2020

International Journal of Antennas and Propagation

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is study sets out an empirical hybrid autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) model designed to estimate electromagnetic wave propagation in densely forested urban areas. Received signal power intensity data was acquired through measurement campaigns carried out in the Metropolitan Area of Belém (MAB), in the Brazilian Amazon. Comparisons were made between estimates from classical least squares (LS) fitting and ITU (International Telecommunication Union) recommendation P. 1546-5. e results indicate the model is, at least, 44% more precise than every ITU estimate and, in some situations, is at least 11% better than an LS estimate, depending on the respective values of the relative error (RE).

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“…Although plenty of single forecast models have been used in different products, scholars have not yet reached a consensus on which one is the best forecasting model in all the cases. In order to improve the individual forecast accuracy as well as to avoid the respective shortcomings within different single models, the concept of "forecast combination" has been regarded as a well-established and well-tested strategy [15,16]. By adding the diversities within the models to be combined, forecast combinations can often generate superior results in contrast to their constituent models [17].…”

Section: Introductionmentioning

confidence: 99%

A Forecast Combination Framework with Multi‐Time Scale for Livestock Products’ Price Forecasting

Ling

Zhang

Mugera

et al. 2019

Mathematical Problems in Engineering

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China’s livestock market has experienced exceptionally severe price fluctuations over the past few years. In this paper, based on the well-established idea of “forecast combination,” a forecast combination framework with different time scales is proposed to improve the forecast accuracy for livestock products. Specifically, we combine the forecasts from multi-time scale, i.e., the short-term forecast and the long-term forecast. Forecasts derived from multi-time scale introduce complementary information about the dynamics of price movements, thus increasing the diversities within the modeling process. Moreover, we investigate a total of ten combination methods with different weighting schemes, including linear and nonlinear combination. The empirical results show that (i) forecast performance can be remarkably improved with this novel combination idea, and short-term forecast model is more suitable for the products with a relatively high volatility, e.g., mutton and beef; (ii) geometric mean, which provides a nonlinear combination, is the most effective one among all the combination methods; and (iii) variance-based weighting scheme can yield a superior result compared to the best individual forecast, especially for the products such as egg and beef.

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A hybrid ETS–ANN model for time series forecasting

Cited by 165 publications

References 27 publications

Demand Forecasting Considering Actual Peak Load Periods Using Artificial Neural Network

Demand Forecasting Considering Actual Peak Load Periods Using Artificial Neural Network

Application of Hybrid ARIMA and Artificial Neural Network Modelling for Electromagnetic Propagation: An Alternative to the Least Squares Method and ITU Recommendation P.1546-5 for Amazon Urbanized Cities

A Forecast Combination Framework with Multi‐Time Scale for Livestock Products’ Price Forecasting

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