Time series analysis on mackerel (scombridae) landings in Malaysia
Mackerel fish is one type of pelagic fish that live in the surface of the ocean. It is also have benefits in terms of protein which also has high demand in Asian and others countries and helps gaining profits in fisheries industries. This study aims to predict mackerel landings in Malaysia in one...
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| 主要な著者: | , , , |
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| フォーマット: | Conference or Workshop Item |
| 言語: | English |
| 出版事項: |
2021
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| 主題: | |
| オンライン･アクセス: | http://eprints.uthm.edu.my/1285/1/P12554_ff1d5a3992155539598c6db17b2ef18c.pdf http://eprints.uthm.edu.my/1285/ https://doi.org/10.30880/ekst.2021.01.01.007 |
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| 要約: | Mackerel fish is one type of pelagic fish that live in the surface of the ocean.
It is also have benefits in terms of protein which also has high demand in Asian and
others countries and helps gaining profits in fisheries industries. This study aims to
predict mackerel landings in Malaysia in one year advance which is 2018. The data
of 132 monthly of mackerel landings from year 2007 until 2017 is used to make a
prediction of mackerels landing by using four methods which are Seasonal
Autoregressive Integrated Moving Average (SARIMA) method, Multiplicative Holt-
Winters, Additive Holt-Winters Method and Simple Exponential Smoothing method.
The aim is to compare the performance among four methods by measuring the
accuracy of each method. The result shows that Additive Holt-Winters method is the
best method used to forecast mackerel landings in 2018 with the lowest value of Mean
Absolute Percentage Error (MAPE) and Mean Square Error (MSE). In conclusion,
the potential result from this study could be used by fish farmers in their annual
planning of supplying fish in Malaysia. |
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