Using Univariate Time Series Models to Forecast Unemployment Rates: Evidence from State of Maine

2016 ◽  
Author(s):  
Kaveh mname Dalvand
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Unemployment Rates ◽  
Univariate Time Series

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 Estimation of bus dwell time using univariate time series models

2014 ◽  
Vol 49 (1) ◽  
pp. 139-152 ◽  
Author(s):  
Soroush Rashidi ◽  
Prakash Ranjitkar
Keyword(s):  
Time Series ◽  
Dwell Time ◽  
Time Series Models ◽  
Univariate Time Series

scholarly journals A COMPARATIVE ANALYSIS OF ALTERNATIVE UNIVARIATE TIME SERIES MODELS IN FORECASTING TURKISH INFLATION

2012 ◽  
Vol 13 (2) ◽  
pp. 275-293 ◽  
Author(s):  
A. Nazif Çatık ◽  
Mehmet Karaçuka
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Unobserved Components ◽  
Inflation Forecasting ◽  
Long Run ◽  
Univariate Time Series ◽  
Forecasting Performance ◽  
Unobserved Components Model ◽  
One Step ◽  
Conventional Models

This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at earlier forecast horizons conventional models, especially ARFIMA and ARIMA, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer in terms of dynamic forecasts. The superiority of the unobserved components model suggests that inflation in Turkey has time varying pattern and conventional models are not able to track underlying trend of inflation in the long run.

scholarly journals Demand For Electricity In Lebanon

2011 ◽  
Vol 8 (1) ◽  
Author(s):  
Salah Abosedra ◽  
Abdallah Dah ◽  
Sajal Ghosh
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Time Span ◽  
Time Series Models ◽  
Detailed Data ◽  
Economic Variables ◽  
Univariate Time Series ◽  
Modeling Techniques ◽  
Socio Economic Variables ◽  
Future Demand

This paper estimates the demand for electricity in Lebanon by employing three modeling techniques namely OLS, ARIMA and exponential smoothing for the time span January 1995 to December 2005. In- sample forecasts reveal that the forecasts made by ARIMA (0,1,3) (1,0,0)12 is superior in terms of lowest RMSE, MSE and MAPE criteria, followed by exponential smoothing and OLS. Therefore, the planners in Lebanon could utilize linear univariate time-series models for forecasting future demand of electricity until detailed data on various socio-economic variables are available, which, in the future, may result in other modeling techniques being superior to estimate the demand for electricity in the country.

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