TY - GEN
T1 - Time series machine learning
T2 - 3rd International Conference on Soft Computing in Data Science, SCDS 2017
AU - Wibowo, Wahyu
AU - Dwijantari, Sarirazty
AU - Hartati, Alia
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - ARIMA
KW - Electricity consumption
KW - Forecasting
KW - Hybrid ARIMA-ANN
UR - http://www.scopus.com/inward/record.url?scp=85036460914&partnerID=8YFLogxK
U2 - 10.1007/978-981-10-7242-0_11
DO - 10.1007/978-981-10-7242-0_11
M3 - Conference contribution
AN - SCOPUS:85036460914
SN - 9789811072413
T3 - Communications in Computer and Information Science
SP - 126
EP - 139
BT - Soft Computing in Data Science - 3rd International Conference, SCDS 2017, Proceedings
A2 - Mohamed, Azlinah
A2 - Yap, Bee Wah
A2 - Berry, Michael W.
PB - Springer Verlag
Y2 - 27 November 2017 through 28 November 2017
ER -
