Forecasting ensemble empirical mode decomposition

How well did the forecast values correspond to the observed values?

Forecasting ensemble empirical mode decomposition

This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract With regard to the nonlinearity and irregularity along with implicit seasonality and trend in the context of air passenger traffic forecasting, this study proposes an ensemble empirical mode decomposition EEMD Forecasting ensemble empirical mode decomposition support vector machines SVMs modeling framework incorporating a slope-based method to restrain the end effect issue occurring during the shifting process of EEMD, which is abbreviated as EEMD-Slope-SVMs.

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Real monthly air passenger traffic series including six selected airlines in USA and UK were collected to test the effectiveness of the proposed approach. Additional evidence is also shown to highlight the improved performance while compared with EEMD-SVM model not restraining the end effect.

Introduction Air passenger traffic forecast is of great importance for airlines and civil aviation authorities. For airlines, accurate forecasts play an increasingly important role in the revenue management. For civil aviation authorities, air passenger traffic forecast provides a concrete basis for planning decisions in air transport infrastructure.

DETR was to present the national forecasts periodically for the future demand for air travel, by passenger numbers, at UK airports as a whole since the s. The last published report was DETR [ 3 ]. Later on, the Department for Transport DFT continued to publish several reports with regard to air passenger demand forecast [ 4 — 6 ].

As the air passenger traffic series are typically considered a nonlinear and nonstationary time series with seasonality, forecasting air passenger traffic remains challenging. In the past decades, academic researchers and practitioners have made many contributions to air passenger traffic forecast.

Most of the quantitative forecasting models abounded in the literature can fall into two categories, namely, econometric modeling and time series. In the econometric modeling area, pioneering works can be found in [ 17 ]. Most econometric models aimed to reveal the relationship between air passenger traffic flow and selected economic or social supply variables such as geoeconomic and service-related factors.

Compared with econometric modeling, little attention has been paid on time series models in air passenger traffic forecast. The important research work was done by Grubb and Mason [ 2 ]. A univariate forecast depends only on the past of the series and not on estimated relationships between the series and exogenous variables, and it does not require forecasts of the exogenous variables, which will themselves be subject to uncertainty.

The same research effort on air passenger traffic forecast by time series methods can be found in the literature [ 8 ].

Due to the complexity of econometric modeling in variables selection and testing, time series approach is a promising alternative in air passenger traffic forecast though they are handicapped by their inability to indentify the causes of air passenger traffic growth with clear interpretation.

Usually, the above time series models can provide good forecasts when the air passenger traffic series under study is linear or near linear with explicit seasonality and trend. However, in real work air passenger traffic series, there is a great deal of nonlinearity and irregularity along with implicit seasonality and trend.

Poor performance can be found frequently in using the traditional time series methods in practice. The main reason is that the underlying assumption of these traditional time series methods is linearity and they cannot capture the nonlinear patterns hidden and recognize the irregularity well.

Recent research efforts on modeling time series with complex nonlinearity, dynamic variation, and high irregularity provided two promising directions.

One is to establish emerging artificial intelligence models such as artificial neural networks ANNssupport vector machines SVMsand genetic programming GP.

The earlier literature on air passenger traffic forecast by ANN can be found in [ 910 ].

Forecasting ensemble empirical mode decomposition

The other is to integrate data decomposition techniques such as empirical mode decomposition EMD or ensemble empirical mode decomposition EEMD, an updated version of EMD into an unified modeling framework to forecast complex nonlinear time series with great fluctuation and irregularity.

Such research effort could be seen in [ 11 — 13 ] and so-called as decomposition-ensemble modeling framework.1. Introduction. Since the early s, the process of deregulation and the introduction of competitive markets have been reshaping the landscape of the traditionally .

Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques decomposition of the original exchange rate series using an ensemble empirical mode decomposition (EEMD) method Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques.

In this project, the aim is to develop a combined model from two completely different computational models for forecasting namely Ensemble Empirical Mode Decomposition and Artificial Neural Network so as to improve accuracy of future predictions of time series data. Load demand forecasting is a critical process in the planning of electric utilities.

An ensemble method composed of Empirical Mode Decomposition (EMD) algorithm and deep learning approach is presented in this work. For this purpose, the load demand series were first decomposed into several intrinsic mode functions (IMFs).

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