Blood Cockle Case Study
1.0 Background Study The cockles farmed in Malaysia and Thailand is bivalves from genes Anadara, family Arcidae. There are not true cockles but more correctly as arc cockles (Tookwinas, 1983). It is usually call as bloody clam. The blood cockle, Anadara granosa usually farmed in muddy and sandy shores in mud flats around mangrove estuary areas. Cockles have a medium size clam-liked shell round and also domed with radiating ridges. Shell equivalve, thick and solid, ovate, strongly inflated, and slightly longer than high and feebly in equilateral (Poutiers, 1998). Wide interstices at each valve of radial ribs are about 18 radial ribs (15 to 20). Tropical bivalve have typical characteristic which have long spawning season with blood cockle. …show more content…
ARIMA was selected because Kwasi and Sharma (2015) have argued that multivariate time series models are better fit models in forecasting of agricultural commodities because it allows for inclusion of other exogenous variables such as rainfall, prices of other markets that have effect on the forecasted time series. In addition, these paper aims are to get accurate forecasting based on several forecasting method and to show that several method should be considered when it comes to agricultural production sector. Generally, ARIMA model is able to capture linear pattern of time series, has greater versatility and better seasonal patterns but requires historical data continuity (Zhang, …show more content…
To predict the production of blood cockles using Auto Regressive Integration Moving Average (ARIMA) Model. 2. To estimate the production of blood cockles using Holt’s Linear Trend 3. To determine and compare which model is better in predicting the production of cockles. 1.4 Scope of Study In this study it will only focus on estimating the production of blood cockle in Malaysia. In this study, there will be hundred and eight of previous data from year 2005 until 2013. The data is obtained from Fisheries Department. Besides that, the software that will be used to estimate the productions is SPSS, Excel, and Minitab. The methods that will be used are Simple exponential smoothing and ARIMA model which is Box Jenkins. This time series model will be used because it have being label as flexible method that use by researcher to forecast time series
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