Modelling of non-scheduled air transportation time series based on ARIMA
Forecasting non-scheduled air transportation demand is essential for effective resource allocation, operational planning, and decision-making. In this paper, the use of the ARIMA (Auto Regressive Integrated Moving Average) model for forecasting non-scheduled air transportation is explored. The ARIMA...
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| Main Authors: | , |
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| Format: | Article |
| Language: | Russian |
| Published: |
Moscow State Technical University of Civil Aviation
2024-12-01
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| Series: | Научный вестник МГТУ ГА |
| Subjects: | |
| Online Access: | https://avia.mstuca.ru/jour/article/view/2464 |
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| Summary: | Forecasting non-scheduled air transportation demand is essential for effective resource allocation, operational planning, and decision-making. In this paper, the use of the ARIMA (Auto Regressive Integrated Moving Average) model for forecasting non-scheduled air transportation is explored. The ARIMA model is a widely employed time series forecasting technique which combines autoregressive (AR), differencing (I), and moving average (MA) components. It has been successfully applied to various fields and can be adapted to capture the patterns and trends in non-scheduled air transportation data. To forecast non-scheduled air transportation demand, historical data, including relevant variables are firstly collected. The data are processed by identifying and addressing any missing values, outliers, or trends that could affect the model's performance. Next, the ARIMA model is applied to the pre-processed data, utilising techniques such as model identification, parameter estimation, and model diagnostics. The ARIMA model captures the relationships between past observations and uses them to predict future demand for non-scheduled air transportation. The forecasting results from the ARIMA model provide insights into expected demand levels, peak periods, and potential fluctuations in non-scheduled air transportation. These forecasts enable decision-makers to optimise resource allocation, schedule aircraft availability, and enhance operational efficiency. However, it is important to note that the accuracy of ARIMA forecasts depends on various factors, including the quality and representativeness of the data, the appropriate selection of model parameters, and the stability of underlying patterns in the time series data. Regular model evaluation and refinement are crucial in maintaining forecasting accuracy. |
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| ISSN: | 2079-0619 2542-0119 |