Time Series Analysis and Prediction Statistical Models for the Duration of the Ship Handling at an Oil Terminal
The main points of this paper are researching of time series and, then, building statistical prediction models based on obtained characteristics. Such investigation is often conducted in medicine and economics. The practice shows, that the studying of such issues in the marine transport logistics area is in demand. Time series analysis comprises methods for analyzing time series data in order to extract meaningful characteristics of the data and forecast future, based on knowledge of the past. The following models are applied to ship handling duration data: exponential smoothing (single, double and triple), and two more sophisticated models – auto-regressive moving average (ARMA) and ARIMA with seasonal component (SARIMA). The choice of a suitable model depends both on the distribution of the data and on the useful information that it will bring. Experiments for every model are carried out with different values of the model parameters to find the best fitting one. For all models Mean Square Error (MSE) is calculated on the training and test data. The best results were achieved by ARMA model with weekly dataset frequency. Nevertheless, there are some other ways to improve prediction models and to obtain more accurate results, which are also proposed in the article.
KeywordsTime series forecasting Statistical models ARMA SARIMA Exponential smoothing Time prediction Ship handling Oil terminal
This work was supported by The Ministry of Education, Youth and Sports of the Czech Republic from the National Programme of Sustainability (NPU II); project IT4Innovations excellence in science – LQ1602.
- 1.Rausch, P., Sheta, A.F., Ayesh, A. (eds.): Business Intelligence and Performance Management: Theory, Systems and Industrial Applications. Springer, London (2013)Google Scholar
- 2.Box, G.E.P., et al.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2015)Google Scholar
- 5.Rudnitckaia, J.: Methods of detection of noises and outliers in data structure on the example of ship handling duration at an oil terminal. Vestnik Gosudarstvennogo Universiteta Morskogo I Rechnogo Flota Imeni Admirala Makarova 4(44), 866–873 (2017). https://doi.org/10.21821/2309-5180-2017-9-4-866-873 CrossRefGoogle Scholar
- 8.NIST/SEMATECH e-Handbook of Statistical Methods. http://www.itl.nist.gov/div898/handbook/
- 9.Brutlag, J.D.: Aberrant behavior detection in time series for network monitoring. In: Proceedings of the 14th USENIX Conference on System Administration, New Orleans, Louisiana (2000)Google Scholar
- 10.Rudnitckaia, J.: Study of descriptive process model of ship handling at an oil terminal based on timesheets. Vestnik Astrakhan State Tech. Univ. 1, 7–15 (2017)Google Scholar