scholarly journals FORECASTING AIR PASSENGER TRAFFIC VOLUME: EVALUATING TIME SERIES MODELS IN LONG-TERM FORECASTING OF KUWAIT AIR PASSENGER DATA

2021 ◽  
Vol 70 (1) ◽  
pp. 69-89
Author(s):  
Ahmad T. Al-Sultan ◽  
Amani Al-Rubkhi ◽  
Ahmad Alsaber ◽  
Jiazhu Pan
Keyword(s):  
Time Series ◽  
Traffic Volume ◽  
Time Series Models ◽  
Passenger Traffic ◽  
Long Term Forecasting

Short, medium and long term forecasting of time series using the L-Co-R algorithm

2014 ◽  
Vol 128 ◽  
pp. 433-446 ◽  
Author(s):  
E. Parras-Gutierrez ◽  
V.M. Rivas ◽  
M. Garcia-Arenas ◽  
M.J. del Jesus
Keyword(s):  
Time Series ◽  
Long Term Forecasting

scholarly journals Improving Inflation Forecasts in the Medium to Long Term

2013 ◽  
pp. 1-6 ◽  
Author(s):  
Saeed Zaman
Keyword(s):  
Time Series ◽  
Forecast Accuracy ◽  
Time Series Models ◽  
Powerful Technique ◽  
Inflation Forecasts ◽  
Statistical Time

A simple but powerful technique for incorporating a changing underlying inflation trend into standard statistical time series models can improve forecast accuracy significantly—about 20 percent to 30 percent, two to three years out.

An integrated deterministic–stochastic approach for forecasting the long-term trajectories of COVID-19

2021 ◽  
pp. 2141001
Author(s):  
Indrajit Ghosh ◽  
Tanujit Chakraborty
Keyword(s):  
Time Series ◽  
Stochastic Approach ◽  
World Population ◽  
Data Sets ◽  
Univariate Time Series ◽  
Reproduction Numbers ◽  
The World ◽  
Long Term Forecasting ◽  
Ar Models

The ongoing coronavirus disease 2019 (COVID-19) pandemic is one of the major health emergencies in decades that affected almost every country in the world. As of June 30, 2020, it has caused an outbreak with more than 10 million confirmed infections, and more than 500,000 reported deaths globally. Due to the unavailability of an effective treatment (or vaccine) and insufficient evidence regarding the transmission mechanism of the epidemic, the world population is currently in a vulnerable position. The daily cases data sets of COVID-19 for profoundly affected countries represent a stochastic process comprised of deterministic and stochastic components. This study proposes an integrated deterministic–stochastic approach to forecast the long-term trajectories of the COVID-19 cases for Italy and Spain. The deterministic component of the daily-cases univariate time series is assessed by an extended version of the SIR [Susceptible–Infected–Recovered–Protected–Isolated (SIRCX)] model, whereas its stochastic component is modeled using an autoregressive (AR) time series model. The proposed integrated SIRCX-AR (ISA) approach based on two operationally distinct modeling paradigms utilizes the superiority of both the deterministic SIRCX and stochastic AR models to find the long-term trajectories of the epidemic curves. Experimental analysis based on the proposed ISA model shows significant improvement in the long-term forecasting of COVID-19 cases for Italy and Spain in comparison to the ODE-based SIRCX model. The estimated Basic reproduction numbers for Italy and Spain based on SIRCX model are found to be [Formula: see text] and [Formula: see text], respectively. ISA model-based results reveal that the number of cases in Italy and Spain between 11 May, 2020–9 June, 2020 will be 10,982 (6383–15,582) and 13,731 (3395–29,013), respectively. Additionally, the expected number of daily cases on 9 July, 2020 for Italy and Spain is estimated to be 30 (0–183) and 92 (0–602), respectively.

Comparative Study of Time Series Models, Support Vector Machines, and GMDH in Forecasting Long-Term Evapotranspiration Rates in Northern Iran

2020 ◽  
Vol 146 (6) ◽  
pp. 04020010 ◽  
Author(s):  
Afshin Ashrafzadeh ◽  
Ozgur Kişi ◽  
Pouya Aghelpour ◽  
Seyed Mostafa Biazar ◽  
Mohammadreza Askarizad Masouleh
Keyword(s):  
Time Series ◽  
Support Vector Machines ◽  
Comparative Study ◽  
Support Vector ◽  
Time Series Models ◽  
Northern Iran ◽  
Vector Machines
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