Forecasting Trend-Seasonal Data Using Nonparametric Regression with Kernel and Fourier Series Approach

Mardianto, M. Fariz Fadillah; Kartiko, Sri Haryatmi; Utami, Herni

doi:10.1007/978-981-13-7279-7_42

Cited by 10 publications

(10 citation statements)

References 9 publications

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“…Therefore, it can be supported with previous study such as Mardianto,et. al.,[2,3,8,[17][18][19][20], Fourier series estimator in nonparametric regression has good performance to estimate pairs of data that have trend -seasonal and oscillation pattern, in this case especially for longitudinal data.…”

Section: Definition 11mentioning

confidence: 99%

“…Data patterns that correspond to the Fourier series approach are repeated data patterns. The process that related with repeating data patterns occurs in the value of the response variable for different predictor variables [3]. Fourier series in nonparametric regression has high flexibility in modeling the relationship between predictors and responses that have oscillation pattern, and combination between trend and seasonal data pattern [4].…”

Section: Introductionmentioning

confidence: 99%

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An Analysis about Fourier Series Estimator in Nonparametric Regression for Longitudinal Data

Mardianto¹,

Gunardi²,

Utami³

2021

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Fourier series is a function that is often used Mathematically and Statistically especially for modeling. Here, Fourier series can be constructed as an estimator in nonparametric regression. Nonparametric regression is not only using cross section data, but also longitudinal data. Some of nonparametric regression estimators have been developed for longitudinal data case, such as kernel, and spline. In this study, we concentrate to develop an inference analysis that related to Fourier series estimator in nonparametric regression for longitudinal data. Nonparametric regression based on Fourier series is capable to model data relationship with fluctuation or oscillation pattern that represents with sine and cosine functions. For point estimation analysis, Penalized Weighted Least Square (PWLS) is used to determine an estimator for parameter vector in nonparametric regression. Different with previous studies, PWLS is used to get smooth estimator. The result is an estimator for nonparametric regression curve for longitudinal data based on Fourier series approach. In addition, this study also investigated the asymptotic properties of the nonparametric regression curve estimators using the Fourier series approach for longitudinal data, especially linearity and consistency. Some study cases based on previous research and a new study case is given to make sure that Fourier series estimator in nonparametric regression has good performance in longitudinal data modeling. This study is important in order to develop further inferences Statistics, such as interval estimation and test hypothesis that related nonparametric regression with Fourier series estimator for longitudinal data.

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Section: Definition 11mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Analysis about Fourier Series Estimator in Nonparametric Regression for Longitudinal Data

Mardianto¹,

Gunardi²,

Utami³

2021

Self Cite

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show abstract

“…Fourier series is a trigonometric function which has show sine and cosine curve. Fourier series is best used on-trend seasonal data [5][6][7][8]. Based on Takezawa [5], a regression model for observation data ( , ) can be written…”

Section: Estimator With Phi (π) Functionmentioning

confidence: 99%

“…If data assumed that the form of regression curve is unknown, it is suggested to use a nonparametric regression approach. Consider paired data, equation of nonparametric regression based on Mardianto et al, [7] given as follows:…”

Section: Estimator Without Phi (π) Functionmentioning

confidence: 99%

“…The procedure in data analysis that is related to estimation contribution of Suroboyo bus with nonparametric regression used complete Fourier series estimator for both models from equation (7), and equation (19) was given as follows: a. Study literature related to the contribution of Suroboyo bus and its relationship to the related predictor variables.…”

Section: Data Analysis Proceduresmentioning

confidence: 99%

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Contribution Analysis of “Suroboyo Bus” in Waste Management Based on Two Form of Complete Fourier Series Estimator

et al. 2021

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Plastic waste is a problem that almost exists in all countries. This problem arises because of the lack of facilities that can handle the plastic waste. Suroboyo Bus is an innovation for this problem because Suroboyo Bus uses plastic bottles as payment. The purpose of this research is to predict the percentage contribution of Suroboyo Bus in handling plastic waste. The Fourier series estimator performs well for data modeling with seasonal trend patterns. This paper examines two approaches to the Fourier series. The difference between the approaches is the inclusion of the phi (π) function in the model. The result shows the goodness of fit criterion model with π function are for and 0,08% for MAPE whereas the fit criterion model without π function is 100% for and 0,07% for MAPE. In conclusion, the Fourier series model without the π function is better because the Fourier series model without the π function is more satisfy the goodness of fit criteria than the Fourier series model with the π.

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Modelling the Price Forecast for Construction Steel: A Case Study in EPC Company

Firdaus

Amrina

2023

E3S Web Conf.

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The EPC (Engineering Procurement Construction) industry is one of the most dynamic industries. The problems faced are related to market conditions that often change, short construction periods, and fluctuations in material prices that are difficult to predict. This dynamic requires an appropriate forecasting model, which can predict the pattern of material price movements and anticipate the occurrence of fluctuations in the future. This research aims to get the best price during the project tender process. This study model the forecasting of construction iron prices in the future by considering the historical pattern of construction iron price data, the value of foreign exchange rates, and the price of billets as raw materials for construction iron. The forecasting procedure used is nonparametric, which involves several statistical tests such as cross-correlation, linearity, and error assessment. The results of this study can be a firm reference for the price value of construction iron, which makes it easier for management to determine an accurate and competitive project value.

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Forecasting Trend-Seasonal Data Using Nonparametric Regression with Kernel and Fourier Series Approach

Cited by 10 publications

References 9 publications

An Analysis about Fourier Series Estimator in Nonparametric Regression for Longitudinal Data

An Analysis about Fourier Series Estimator in Nonparametric Regression for Longitudinal Data

Contribution Analysis of “Suroboyo Bus” in Waste Management Based on Two Form of Complete Fourier Series Estimator

Modelling the Price Forecast for Construction Steel: A Case Study in EPC Company

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