From in-situ measurement to regression and time series models: An overview of trends and prospects for building performance modelling

In situ measurement of time-series two dimensional O2 distributions at sediment-water interface using a planar O2 optode system connected with a submarine cable

2007 Symposium on Underwater Technology and Workshop on Scientific Use of Submarine Cables and Related Technologies ◽

10.1109/ut.2007.370814 ◽

2007 ◽

Cited By ~ 1

Author(s):

Kazumasa Oguri ◽

Hiroshi Kitazato ◽

Ronnie N. Glud ◽

Henrik Stahl ◽

Frank Wenzhofer ◽

...

Keyword(s):

Time Series ◽

In Situ Measurement ◽

Water Interface ◽

Two Dimensional ◽

Submarine Cable

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Building performance monitoring: from in-situ measurement to regression-based approaches

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/609/7/072043 ◽

2019 ◽

Vol 609 ◽

pp. 072043 ◽

Cited By ~ 1

Author(s):

Lamberto Tronchin ◽

Massimiliano Manfren ◽

Vincenzo Vodola ◽

Benedetto Nastasi

Keyword(s):

Performance Monitoring ◽

In Situ Measurement ◽

Building Performance

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Time-Series Models in Marketing: Some Recent Developments

Marketing ZFP ◽

10.15358/0344-1369-2010-jrm-1-24 ◽

2010 ◽

Vol 32 (JRM 1) ◽

pp. 24-29

Author(s):

Marnik G. Dekimpe ◽

Dominique M. Hanssens

Keyword(s):

Time Series ◽

Time Series Models ◽

Recent Developments

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A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

MALAYSIAN JOURNAL OF COMPUTING ◽

10.24191/mjoc.v5i1.6760 ◽

2020 ◽

Vol 5 (1) ◽

pp. 374

Author(s):

Pauline Jin Wee Mah ◽

Nur Nadhirah Nanyan

Keyword(s):

Time Series ◽

Palm Oil ◽

Moving Average ◽

Forecast Accuracy ◽

Peninsular Malaysia ◽

Time Series Models ◽

Crude Palm Oil ◽

Univariate Time Series ◽

Import And Export ◽

Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA emerged the best model based on the RMSE value. When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

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