scholarly journals Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Wei Ming ◽  
Yukun Bao ◽  
Zhongyi Hu ◽  
Tao Xiong
Keyword(s):  
Prediction Models ◽  
Nonlinear Modeling ◽  
Real Data ◽  
Data Preprocessing ◽  
Air Transportation ◽  
Data Sets ◽  
Term Prediction ◽  
Short Term Prediction ◽  
Long Term Prediction

The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic with the two most commonly used multistep-ahead prediction strategies, that is, iterated strategy and direct strategy. Additionally, the effectiveness of data preprocessing approaches, such as deseasonalization and detrending, is investigated and proofed along with the two strategies. Real data sets including four selected airlines’ monthly series were collected to justify the effectiveness of the proposed approach. Empirical results demonstrate that the direct strategy performs better than iterative one in long term prediction case while iterative one performs better in the case of short term prediction. Furthermore, both deseasonalization and detrending can significantly improve the prediction accuracy for both strategies, indicating the necessity of data preprocessing. As such, this study contributes as a full reference to the planners from air transportation industries on how to tackle multistep-ahead prediction tasks in the implementation of either prediction strategy.

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

Long-term prediction on atmospheric corrosion data series of carbon steel in China based on NGBM(1,1) model and genetic algorithm

2019 ◽  
Vol 66 (4) ◽  
pp. 403-411 ◽  
Author(s):  
Yuanjie Zhi ◽  
Dongmei Fu ◽  
Tao Yang ◽  
Dawei Zhang ◽  
Xiaogang Li ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Carbon Steel ◽  
Atmospheric Corrosion ◽  
Data Series ◽  
Data Sets ◽  
Content Type ◽  
Term Prediction ◽  
Long Term Prediction ◽  
Corrosion Data

PurposeThis study aims to achieve long-term prediction on a specific monotonic data series of atmospheric corrosion rate vs time.Design/methodology/approachThis paper presents a new method, used to the collected corrosion data of carbon steel provided by the China Gateway to Corrosion and Protection, that combines non-linear gray Bernoulli model (NGBM(1,1) with genetic algorithm to attain the purpose of this study.FindingsResults of the experiments showed that the present study’s method is more accurate than other algorithms. In particular, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the proposed method in data sets are 9.15 per cent and 1.23 µm/a, respectively. Furthermore, this study illustrates that model parameter can be used to evaluate the similarity of curve tendency between two carbon steel data sets.Originality/valueCorrosion data are part of a typical small-sample data set, and these also belong to a gray system because corrosion has a clear outcome and an uncertainly occurrence mechanism. In this work, a new gray forecast model was proposed to achieve the goal of long-term prediction of carbon steel in China.

scholarly journals Novel Robust Time Series Analysis for Long-term and Short-term Prediction

2021 ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

Abstract Nonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with traditional least squares and least absolute deviations methods using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner--recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas the other methods fail to estimate autocorrelation accurately.

Oblique-incidence ionospheric soundings over Central Europe and their application for testing now casting and long term prediction models

2009 ◽  
Vol 43 (11) ◽  
pp. 1611-1620 ◽  
Author(s):  
M. Pietrella ◽  
L. Perrone ◽  
G. Fontana ◽  
V. Romano ◽  
A. Malagnini ◽  
...  
Keyword(s):  
Central Europe ◽  
Oblique Incidence ◽  
Prediction Models ◽  
Term Prediction ◽  
Long Term Prediction

Crop maturity in relation to quality of canned cream style sweet corn

1951 ◽  
Vol 2 (1) ◽  
pp. 43
Author(s):  
RS Mitchell ◽  
LJ Lynch
Keyword(s):  
Sweet Corn ◽  
Moisture Loss ◽  
Term Prediction ◽  
Grain Moisture ◽  
Heat Increment ◽  
Moisture Percentage ◽  
Short Term Prediction ◽  
Long Term Prediction

The reliability of grain moisture percentage as a measure of desirable picking maturity was confirmed. The refractive index of the fluid material expressed from the grain was shown to give a rapid estimation of the grain moisture, the accuracy being sufficient for commercial requirements. The rate of moisture loss from ripening grain grown at Windsor, N.S.W., was measured and used for the short-term prediction of the date of harvest. Limits were defined for different planting times in terms of the number of days to picking maturity. Such limits provide a simpler means of long-term prediction than the heat increment method proposed by American workers. A plan is outlined for the field control of commercial sweet corn plantings in the Windsor district.

Fusion-based Satellite Clock Bias Prediction Considering Characteristics and Fitted Residue

2018 ◽  
Vol 71 (4) ◽  
pp. 955-970 ◽  
Author(s):  
Jicang Lu ◽  
Chao Zhang ◽  
Yong Zheng ◽  
Ruopu Wang
Keyword(s):  
Prediction Models ◽  
Prediction Method ◽  
Satellite System ◽  
Satellite Clock ◽  
Term Prediction ◽  
Mode Decomposition ◽  
Satellite Clock Bias ◽  
Global Navigation Satellite ◽  
Long Term Prediction

As Satellite Clock Bias (SCB) prediction may be affected by various factors such as periodic items, sampling length, and stochastic items, a fusion-based prediction method is proposed by considering characteristics of SCB and fitted residue. On this basis, an instance algorithm is presented by fusing four typical prediction models. First, we use Empirical Mode Decomposition (EMD) to pre-process and decompose the SCB series into multiple components with various characteristics. Then, we analyse the fitting performance of each model for different components and prediction length, namely short-, mid- and long-term prediction, and select models with the best performance. Next, we analyse fitted residue of the reconstructed SCB, and select the model with the best fitting results. Finally, we fuse the multiple selected models for SCB prediction. The method is tested using Global Positioning System (GPS) precise clock products provided by the International Global Navigation Satellite System Service (IGS). Experimental results show that, compared with single prediction models and existing combination models, the proposed fusion-based prediction method improves accuracy and stability. In particular, the proposed method is more stable and has better performance for mid- and long-term prediction.

Durability performance and long-term prediction models of sand-coated basalt FRP bars

2019 ◽  
Vol 157 ◽  
pp. 248-258 ◽  
Author(s):  
Ahmed H. Ali ◽  
Hamdy M. Mohamed ◽  
Brahim Benmokrane ◽  
Adel ElSafty ◽  
Omar Chaallal
Keyword(s):  
Prediction Models ◽  
Term Prediction ◽  
Long Term Prediction ◽  
Durability Performance ◽  
Frp Bars
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