Time Series Analysis and Prediction Statistical Models for the Duration of the Ship Handling at an Oil Terminal

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F17%3APU127282" target="_blank" >RIV/00216305:26230/17:PU127282 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-319-74454-4_12" target="_blank" >http://dx.doi.org/10.1007/978-3-319-74454-4_12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-74454-4_12" target="_blank" >10.1007/978-3-319-74454-4_12</a>

Result language

angličtina

Original language name

Time Series Analysis and Prediction Statistical Models for the Duration of the Ship Handling at an Oil Terminal

Original language description

This work relates to the whole series of papers aimed at creating a marine transport and logistics process map. This map is a reflection of a real process model (descriptive model) with the possibility of extension (scaling process), determination bottlenecks (traffic jam), detecting of deviations for operational response, representation of different perspectives (control-flow, resources, performance). Also, the map can be used as a basis for prediction and decision making systems. As the object of the study, the port module was chosen, namely its component part - the oil terminal. The analysed process includes the whole ship handling from the moment of its arrival to the port (activity Notice received) till the departure (operation Pilotage). Today there are a huge number of ways to model the processes and the main aim is searching of optimal and effective methods of modern intelligent analysis (from the field of Machine Learning, Data Mining, statistics, Process Mining) for building a process map.  The main point of this paper is to conduct research of time series and, then, to build statistical prediction model based on obtained characteristics. At the beginning of the article, the analysed time series is presented, which shows the distribution of the ship handling duration for the last 3 years. The main components of the time series, an explanation of their values and their effect on the prediction model are given below. In this article, the famous statistical model auto regression integrated moving average (ARIMA) was chosen for the prediction. The paper presents the results of its application to the port data, the advantages and disadvantages are indicated.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

RELIABILITY and STATISTICS in TRANSPORTATION and COMMUNICATION

ISBN

978-9984-818-86-3

ISSN

2367-3370

e-ISSN

—

Number of pages

10

Pages from-to

127-136

Publisher name

Springer International Publishing

Place of publication

Riga

Event location

Riga

Event date

Sep 19, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000434081600012