Publication detail

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

RUDNITCKAIA, J. HRUŠKA, T.

Original Title

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

English Title

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

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

time series, statistical models, ARIMA, time prediction, ship handling, oil terminal

Released

30.11.2017

Publisher

Springer International Publishing

Location

Riga

ISBN

978-9984-818-86-3

Book

RELIABILITY and STATISTICS in TRANSPORTATION and COMMUNICATION

Edition

Lecture Notes in Networks and Systems

Edition number

NEUVEDEN

Pages from

127

Pages to

136

Pages count

10

Documents

BibTex


@inproceedings{BUT146268,
  author="Julia {Rudnitckaia} and Tomáš {Hruška}",
  title="Time Series Analysis and Prediction Statistical Models for the Duration of the Ship Handling at an Oil Terminal",
  annote="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.",
  address="Springer International Publishing",
  booktitle="RELIABILITY and STATISTICS in TRANSPORTATION and COMMUNICATION",
  chapter="146268",
  doi="10.1007/978-3-319-74454-4_12",
  edition="Lecture Notes in Networks and Systems",
  howpublished="online",
  institution="Springer International Publishing",
  number="36",
  year="2017",
  month="november",
  pages="127--136",
  publisher="Springer International Publishing",
  type="conference paper"
}