Normal view MARC view ISBD view

Time series analysis of swine production in Calabarzon / by Josephine C. Añasco.

By:

Añasco, Josephine C [author]

Contributor(s):

Mojica, Jennifer R [adviser]

Material type: Text

TextLanguage: English Publication details: Indang, Cavite : 2016. Cavite State University- Main Campus,Description: xiii, 69 pages : illustrations ; 28 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

Subject(s):

DDC classification:

636.4 Añ1 2016

Online resources:

Click here to view thesis abstract and table of contents

Production credits:

College of Arts and Science (CAS)

Abstract: ANASCO, JOSEPHINE CALISIN. Time Series Analysis of Swine Production in CALABARZON. An Undergraduate Thesis. Bachelor of Science in Applied Mathematics with specialization in Statistics. Cavite State University, Indang, Cavite. April 2016. Adviser: Mrs. Jennifer R. Mojica. The study was conducted to 1) formulate a model to use in predicting swine production in CALABARZON; 2) test the adequacy of the formulated model; and 3) predict the quarterly swine production in CALABARZON from 2016 to 2017. The quarterly series data of the swine production in CALABARZON from 1994 to 2015 was obtained from the Philippine Statistics Authority (PSA) at Trece Martires City, Cavite. Seven models were formulated in the study. To select the best model that would predict the quarterly swine production in CALABARZON from 2016 to 2017, the following criteria were considered: R-squared; Standard Error of Regression, Residual Sum of Squares, Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC). Quarterly forecasted values from the year 2016 to 2017 were computed using the formulated model ARIMA (3,2,1) with the equation: A2 9t = 1.318A2 1nyt_1 — 0.390A21nyt-2 + 0.236A21nyt-3 + 0.426A21nyt-4 - 0.590A21nyt-5 - 0.423A21net-1. The forecasted values are 1 658 315, 1 704 537, 1 703 605, 1 711 747, 1 733 912, 1 768 379, 1 780 743 and 1 789 849 respectively. The forecast accuracy was also tested using the Mean Absolute Percentage Error (MAPE) method. The MAPE results shows that there was a small standard error indicating that the forecasted values are reliable.

Average rating: 0.0 (0 votes)

Holdings ( 1 )
Title notes ( 5 )

Holdings
Item type	Current library	Collection	Call number	Materials specified	URL	Status	Notes	Date due	Barcode
Theses / Manuscripts	Ladislao N. Diwa Memorial Library Theses Section	Non-fiction	636.4 Añ1 2016 (Browse shelf(Opens below))		Link to resource	Room use only	T-6201		00010026

Thesis (BS Applied Mathematics) Cavite State University

Includes bibliographical references.

College of Arts and Science (CAS)

ANASCO, JOSEPHINE CALISIN. Time Series Analysis of Swine Production in
CALABARZON. An Undergraduate Thesis. Bachelor of Science in Applied Mathematics with specialization in Statistics. Cavite State University, Indang, Cavite. April 2016. Adviser: Mrs. Jennifer R. Mojica.

The study was conducted to 1) formulate a model to use in predicting swine production in CALABARZON; 2) test the adequacy of the formulated model; and 3) predict the quarterly swine production in CALABARZON from 2016 to 2017.

The quarterly series data of the swine production in CALABARZON from 1994 to 2015 was obtained from the Philippine Statistics Authority (PSA) at Trece Martires City, Cavite.

Seven models were formulated in the study. To select the best model that would predict the quarterly swine production in CALABARZON from 2016 to 2017, the following criteria were considered: R-squared; Standard Error of Regression, Residual Sum of Squares, Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC).

Quarterly forecasted values from the year 2016 to 2017 were computed using the formulated model ARIMA (3,2,1) with the equation: A2 9t = 1.318A2 1nyt_1 — 0.390A21nyt-2 + 0.236A21nyt-3 + 0.426A21nyt-4 - 0.590A21nyt-5 - 0.423A21net-1. The forecasted values are 1 658 315, 1 704 537, 1 703 605, 1 711 747, 1 733 912, 1 768 379, 1 780 743 and 1 789 849 respectively.

The forecast accuracy was also tested using the Mean Absolute Percentage Error (MAPE) method. The MAPE results shows that there was a small standard error indicating that the forecasted values are reliable.

Submitted copy to the University Library. 02/14/2017 T-6201