Prediction of Marine Traffic Density Using Different Time Series Model From AIS data of Port Klang and Straits of Malacca

In the study of ocean engineering, marine traffic is referring to the study of the pattern of the density of ships within the particular boundaries at certain periods. The Port Klang and Straits of Malacca are known for one of the heaviest traffics in Malaysia and the world. The study of traffic within this area is important, because it enables ships to avoid traffic congestion that might happen. Thus, this study is mainly aimed at predicting or forecasting the density of the ships using the route through this waterway by using quantitative methods which are time-series models and the associative models from the Automatic Identification System (AIS) data. The moving averages, weight moving average, and exponential smoothing for the time series model and associative model have used multiple regression. The results show an exponential smoothing alpha 0.8 and give the lowest MAPE as 20.701%, thereby making this method to be the best in forecasting the future traffic density among the method categories.

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Mapping Vessel Path of Marine Traffic Density of Port Klang, Malaysia using Automatic Identification System (AIS) Data

International Journal of Science and Research (IJSR) ◽

10.21275/v4i11.nov151099 ◽

2015 ◽

Vol 4 (11) ◽

pp. 245-248

Keyword(s):

Traffic Density ◽

Automatic Identification ◽

Identification System ◽

Automatic Identification System ◽

Marine Traffic ◽

Port Klang

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A forecasting the consumer price index using time series model

SHS Web of Conferences ◽

10.1051/shsconf/202110710002 ◽

2021 ◽

Vol 107 ◽

pp. 10002

Author(s):

Volodymyr Shinkarenko ◽

Alexey Hostryk ◽

Larysa Shynkarenko ◽

Leonid Dolinskyi

Keyword(s):

Time Series ◽

Price Index ◽

Consumer Price Index ◽

Exponential Smoothing ◽

Time Series Model ◽

Initial Time ◽

Autocorrelation Functions ◽

Minimum Error ◽

Initial Time Series

This article examines the behavior of the consumer price index in Ukraine for the period from January 2010 to September 2020. The characteristics of the initial time series, the analysis of autocorrelation functions made it possible to reveal the tendency of their development and the presence of annual seasonality. To model the behavior of the consumer price index and forecast for the next months, two types of models were used: the additive ARIMA*ARIMAS model, better known as the model of Box-Jenkins and the exponential smoothing model with the seasonality estimate of Holt-Winters. As a result of using the STATISTICA package, the most adequate models were built, reflecting the monthly dynamics of the consumer price index in Ukraine. The inflation forecast was carried out on the basis of the Holt-Winters model, which has a minimum error.

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PENERAPAN METODE EXPONENTIAL SMOOTHING UNTUK PERAMALAN JUMLAH KLAIM DI BPJS KESEHATAN PAMEKASAN

Jurnal Matematika MANTIK ◽

10.15642/mantik.2016.2.1.46-51 ◽

2016 ◽

Vol 2 (1) ◽

pp. 46 ◽

Cited By ~ 1

Author(s):

Faisol Faisol ◽

Sitti Aisah

Keyword(s):

Time Series ◽

Average Method ◽

Moving Average ◽

Exponential Smoothing ◽

Time Series Model ◽

Smoothing Method ◽

Repair Procedure ◽

Double Exponential Smoothing ◽

Using Data ◽

Exponential Smoothing Method

Time series model is the model used to predict the future using past data, one example of a time series model is exponential smoothing. Exponential smoothing method is a repair procedure performed continuously at forecasting the most recent data. In this study the exponential smoothing method is applied to predict the number of claims in the health BPJS Pamekasan using data from the period January 2014 to December 2015, the measures used to obtain the output of this research there are four stages, namely 1) the identification of data, 2) Modeling, 3) forecasting, 4) Evaluation of forecasting results with RMSE and MAPE. Based on the research methodology, the result for the period 25 = 833.828, the 26 = 800.256, period 27 = 766.684, a period of 28 = 733.113, period 29 = 699.541, and the period of 30 = 655, 970. Value for RMSE = 98.865 and MAPE = 7.002, In this case the moving average method is also used to compare the results of forecasting with double exponential smoothing method. Forecasting results for the period 25 = 899.208, the 26 = 885, 792, 27 = 872.375 period, a period of 28 = 858.958, period 29 = 845.542, and the period of 30 = 832.125. Value for RMSE = 101.131 and MAPE = 7.756. Both methods together - both have very good performance because the value of MAPE is below 10%, but the method of exponential smoothing has a value of RMSE and MAPE are smaller than the moving average method.

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Mapping Marine Traffic Density by Using AIS Data: An Application in the Northern Aegean Sea

Polish Maritime Research ◽

10.2478/pomr-2018-0131 ◽

2018 ◽

Vol 25 (4) ◽

pp. 49-58 ◽

Cited By ~ 1

Author(s):

Burak Kundakçi ◽

Selçuk Nas

Keyword(s):

Data Storage ◽

Aegean Sea ◽

Traffic Density ◽

Automatic Identification ◽

Identification System ◽

Cross Sectional ◽

Marine Traffic ◽

Desktop Application ◽

Intersection Algorithm ◽

Northern Aegean Sea

Abstract Automatic Identification System (AIS) data is used for monitoring the movements of vessels live movements through instant transmission of vessel information while, at the same time, historical AIS data is used for marine traffic analysis by researchers. There are several methods and computer programs developed for the analysis of marine traffic by the use of AIS data. Combining the intersection algorithm proposed by Antonio (1992) and distance calculation method, this study develops a method to analyse vessel distribution on a selected cross sectional line (SCS) in the Northern Aegean Sea. As a complementary to the new methods proposed, a desktop application is developed in C# programming language to visualize the vessel distribution on the SCS line. SQL server is used for AIS data storage and analysis. The study is conducted over 7-day AIS data, specifically 2.382.469 rows and 42.884.442 data in total, belonging to the Northern Aegean Sea marine traffic. As a result, the mapping of the movements of different types of vessels in the Northern Aegean Sea is effectively performed and Frequency-Distance, Draught-Distance, SOG-Distance, SOG-COG distributions on the SCS line are successfully analysed by the new method introduced.

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TIME SERIES MODEL FOR CARBON MONOXIDE (CO) AT SEVERAL INDUSTRIAL SITES IN PENINSULAR MALAYSIA

MALAYSIAN JOURNAL OF COMPUTING ◽

10.24191/mjoc.v4i1.5550 ◽

2019 ◽

Vol 4 (1) ◽

pp. 246

Author(s):

Norshahida Shaadan ◽

Muhammad Soffi Rusdi ◽

Nik Noorul Syakirin Nik Mohd Azmi ◽

Shahira Fazira Talib ◽

Wan Athirah Wan Azmi

Keyword(s):

Air Pollution ◽

Time Series ◽

Carbon Monoxide ◽

Peninsular Malaysia ◽

Time Series Model ◽

Traffic Density ◽

Daily Maximum ◽

Industrial Sites ◽

Long Term Effect ◽

Main Component

Malaysia is reported to experience explosive rise in the demand of transport vehicles in recent years due to rapid economic development and population growth. As a result, air pollution is expected to increase in conjunction with the increase in the number of the vehicles. In particular, Carbon Monoxide (CO) has been identified as the main component of the emission sources from vehicles other than Nitrogen Oxide (NOx), hydrocarbon lead and particulate matter of size less than 10 micron (PM10). This provides the reason why CO concentration is often used to reflect traffic density in an area. CO has both short-term and long-term effect on human’s health. Thus, knowledge on CO behaviour and the future levels at an area is important to help decision makers in managing air pollution due to vehicles emission in the country. This study was conducted to describe CO data and to determine a suitable time series model to enable the prediction of CO levels at two industrial sites; Perai and Pasir Gudang, Malaysia. The model obtained could help management to mitigate CO pollution at the sites. The analysis was conducted using daily maximum data which was obtained from the Department of Environment Malaysia from 2010 to 2014. The performance of the best model was determined using several performance measures such as MAE, RMSE and MAPE. The study has found that the most appropriate time series model for Perai is ARIMA (3,1,1) and for Pasir Gudang is SARIMA (2, 1, 8) (1, 1, 2)7.

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