scholarly journals Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models

2022 ◽  
Vol 130 (3) ◽  
pp. 1517-1532
Author(s):  
W. A. Shaikh ◽  
S. F. Shah ◽  
S. M. Pandhiani ◽  
M. A. Solangi
Keyword(s):  
Time Series ◽  
Wavelet Decomposition ◽  
Time Series Models

Time-Series Models in Marketing: Some Recent Developments

Marketing ZFP ◽  
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

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.

scholarly journals Lagrangian time series models for ocean surface drifter trajectories

2015 ◽  
Vol 65 (1) ◽  
pp. 29-50 ◽  
Author(s):  
Adam M. Sykulski ◽  
Sofia C. Olhede ◽  
Jonathan M. Lilly ◽  
Eric Danioux
Keyword(s):  
Time Series ◽  
Ocean Surface ◽  
Time Series Models ◽  
Surface Drifter

scholarly journals Modelling spreading of an infection using time series by a novel family of models; fitting the time series of the confirmed cases of COVID-19 in China

2020 ◽  
Vol 9 (s1) ◽  
Author(s):  
Babak Jamshidi ◽  
Shahriar Jamshidi Zargaran ◽  
Mansour Rezaei
Keyword(s):  
Infectious Disease ◽  
Time Series ◽  
Communicable Disease ◽  
Time Series Models ◽  
Cumulative Number ◽  
The Novel ◽  
New Family ◽  
Two Samples ◽  
Almost All ◽  
Number Of Individuals

AbstractIntroductionTime series models are one of the frequently used methods to describe the pattern of spreading an epidemic.MethodsWe presented a new family of time series models able to represent the cumulative number of individuals that contracted an infectious disease from the start to the end of the first wave of spreading. This family is flexible enough to model the propagation of almost all infectious diseases. After a general discussion on competent time series to model the outbreak of a communicable disease, we introduced the new family through one of its examples.ResultsWe estimated the parameters of two samples of the novel family to model the spreading of COVID-19 in China.DiscussionOur model does not work well when the decreasing trend of the rate of growth is absent because it is the main presumption of the model. In addition, since the information on the initial days is of the utmost importance for this model, one of the challenges about this model is modifying it to get qualified to model datasets that lack the information on the first days.

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