An improved algorithm for the detection of dynamical interdependence in bivariate time-series

2003 ◽  
Vol 88 (2) ◽  
pp. 129-136 ◽  
Author(s):  
John R. Terry ◽  
Michael Breakspear
Keyword(s):  
Time Series ◽  
Bivariate Time Series ◽  
Improved Algorithm

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

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.

Common Factors in Conditional Distributions for Bivariate Time Series

2003 ◽  
Author(s):  
Clive W. J. Granger ◽  
Timo Teräsvirta ◽  
Andrew J. Patton
Keyword(s):  
Time Series ◽  
Common Factors ◽  
Conditional Distributions ◽  
Bivariate Time Series

Trend-Based Time Series Prediction Algorithm

2010 ◽  
Vol 108-111 ◽  
pp. 1164-1169
Author(s):  
Xin Qi ◽  
Hong Liang ◽  
Zhen Li
Keyword(s):  
Time Series ◽  
Monitoring System ◽  
Time Series Prediction ◽  
Prediction Algorithm ◽  
Short Term ◽  
Grid Monitoring ◽  
Status Information ◽  
Term Performance ◽  
Improved Algorithm

According to the resources performance and status information provided by grid monitoring system, this paper adopts a trend-based time series prediction algorithm to predict short-term performance of the resources. Experiments show that the improved mixed trend-based prediction algorithm tracks the trend of data changes by giving more weight, simultaneously takes the different situations of data increases and decreases into account, so the improved algorithm is superior to the pre-improved and it improves the accuracy of the prediction effectively.

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