Wahyu Wibowo

Time Series Machine Learning: Implementing ARIMA and Hybrid ARIMA-ANN for Electricity Forecasting Modeling

The aims of this paper are to develop a linear and nonlinear model in time series to forecast electricity consumption of the lowest household category in East Java, Indonesia. The installed capacity in the lowest household customer category has various power, i.e. 450 VA, 900 VA, 1300 VA, and 2200 VA. ARIMA models are family of linear model for time series analysis and forecasting for both stationary and non-stationary, seasonal and non-seasonal time series data. A nonlinear time series model is proposed by hybrid ARIMA-ANN, a Radial Basis Function using orthogonal least squares. The criteria used to choose the best forecasting model are the Mean Absolute Percentage Error and the Root Mean Square Error. The ARIMA best model are ARIMA ([1, 2], 1, 0) (0, 1, 0)12, ARIMA (0, 1, 1) (0, 1, 0)12, ARIMA (0, 1, 1) (0, 1, 0)12, ARIMA (1, 0, 0) (0, 1, 0)12 respectively. The ANN architecture optimum are ANN (2, 12, 1), ANN (1, 12, 1), ANN (1, 12, 1), and ANN (1, 12, 1). The best models are ARIMA ([1, 2], 1, 0) (0, 1, 0)12, ARIMA (0, 1, 1) (0, 1, 0)12, ANN (1, 12, 1), and ANN (1, 12, 1) in each category respectively. Hence, the result shows that a complex model is not always better than a simpler model. Additionally, a better hybrid ANN model is relied on the choice of a weighted input constant of RBF.

Multiresponse semiparametric regression for modelling the effect of regional socio-economic variables on the use of information technology

Multiresponse semiparametric regression is simultaneous equation regression model and fusion of parametric and nonparametric model. The regression model comprise several models and each model has two components, parametric and nonparametric. The used model has linear function as parametric and polynomial truncated spline as nonparametric component. The model can handle both linearity and nonlinearity relationship between response and the sets of predictor variables. The aim of this paper is to demonstrate the application of the regression model for modeling of effect of regional socio-economic on use of information technology. More specific, the response variables are percentage of households has access to internet and percentage of households has personal computer. Then, predictor variables are percentage of literacy people, percentage of electrification and percentage of economic growth. Based on identification of the relationship between response and predictor variable, economic growth is treated as no...

Path, centroid, and factor scheme for modeling the remuneration of educational staff in ITS with partial least square (PLS)

Bureaucracy condition in Indonesia reveals many shortcomings. One of bureaucratic reformation from the government is the remuneration for Civil Servants (PNS). Remuneration is a part of welfare received by employees, which can be used as an element of motivation for employees to excel and improve their performance. Variables in this study are interrelated. Motivation for achievement (ξ1), characteristics of work environment (ξ2) and training transfer (ξ3) are supposedly affect the performance (η1), while the performance (η1) affects the remuneration (η2). Both the performance and remuneration are constructs or latent variables, which cannot be measured directly. Therefore, the SEM method is considered able to resolve these problems. However, SEM has some assumptions that must be met. The assumptions were frequently violated when real data is used, so we need a method that is free of assumptions, free distribution and flexible that is variance-based SEM or namely partial least square (PLS). PLS is an estim...

Simulation study for model performance of multiresponse semiparametric regression

The objective of this paper is to evaluate the performance of multiresponse semiparametric regression model based on both of the function types and sample sizes. In general, multiresponse semiparametric regression model consists of parametric and nonparametric functions. This paper focuses on both linear and quadratic functions for parametric components and spline function for nonparametric component. Moreover, this model could also be seen as a spline semiparametric seemingly unrelated regression model. Simulation study is conducted by evaluating three combinations of parametric and nonparametric components, i.e. linear-trigonometric, quadratic-exponential, and multiple linear-polynomial functions respectively. Two criterias are used for assessing the model performance, i.e. R-square and Mean Square Error (MSE). The results show that both of the function types and sample sizes have significantly influenced to the model performance. In addition, this multiresponse semiparametric regression model yields th...