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Time series analysis of banana production in the Philippines / by Tisha Lynd G. Viray.

By:

Viray, Tisha Lynd G [author]

Contributor(s):

Cordial, Renelyn R [adviser]

Material type: Text

TextLanguage: English Publication details: Indang, Cavite : 2016. Cavite State University- Main Campus,Description: xiv, 68 pages : 28 cm. illustrationsContent type:

text

Media type:

unmediated

Carrier type:

volume

Subject(s):

DDC classification:

658.5 V81 2016

Online resources:

Click here to view thesis abstract and table of contents

Production credits:

College of Arts and Science (CAS)

Abstract: VIRAY, TISHA LYND GUEVARRA. Time Series Analysis of Banana Production in the Philippines. Undergraduate Thesis. Bachelor of Science in Applied Mathematics with specialization in Statistics. College of Arts and Sciences, Cavite State University, Indang, Cavite. April 2016. Adviser: Dr. Renelyn R. Cordial. The study was undertaken to determine the appropriate ARIMA model to describe the annual production of banana in the Philippines. Specifically, the study aimed to formulate an Autoregressive Integrated Moving Average (ARIMA) model that can be used to forecast banana production in the Philippines; test the adequacy of the formulated model; generate forecasts of banana production from 2015-2020 and determine the forecast ability of the fitted model. This study aimed to be beneficial to different sectors such as government, policy makers, exporters, producers, importing countries and future researchers. The data used in the study were secondary data from the website of Bureau of Agricultural Statistics (BAS) of Philippines Statistics Authority (PSA). Model was selected using time series analysis. Three models were formulated in this study. To select the best model that would forecast the banana production of Philippines from 2015 to 2020, the following criteria were considered: R- Squared; Adjusted R-Squared; standard error of regression; sum of squared residual; Durbin-Watson statistics; Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC). Forecasted values from the year 2015 to 2020 were computed using the formulated model IMA (0, 2, l) with the equation: A 2 1nyt = 0.361918A2 1net-1 + 4.276164A 2 1net-2 — 1.638082A 2 1net The forecasted banana production in the Philippines for the year 2015 to 2020 followed a consistent trend. For the year 2015, the banana production was approximate by be metric tons; 9,289,968.7 metric tons in 2016; 9,543,518.6 metric tons in 2017; 9,796,375.8 metric tons in 2018; 10,049,232.9 metric tons in 2019; and 10,302,090 metric tons in 2020. The forecast accuracy was also tested using Mean Absolute Percentage Error (MAPE) method and Theil 's U Statistics. The MAPE results showed that there was a small standard error, the computed Theil's U statistics was less than value 1. This was an indication that the forecast was reliable.

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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	658.5 V81 2016 (Browse shelf(Opens below))		Link to resource	Room use only	T-6204		00010018

Thesis (BS Applied Mathematics) Cavite State University

Includes bibliographical references.

College of Arts and Science (CAS)

VIRAY, TISHA LYND GUEVARRA. Time Series Analysis of Banana Production in the Philippines. Undergraduate Thesis. Bachelor of Science in Applied Mathematics with specialization in Statistics. College of Arts and Sciences, Cavite State University, Indang, Cavite. April 2016. Adviser: Dr. Renelyn R. Cordial.

The study was undertaken to determine the appropriate ARIMA model to describe the annual production of banana in the Philippines. Specifically, the study aimed to formulate an Autoregressive Integrated Moving Average (ARIMA) model that can be used to forecast banana production in the Philippines; test the adequacy of the formulated model; generate forecasts of banana production from 2015-2020 and determine the forecast ability of the fitted model. This study aimed to be beneficial to different sectors such as government, policy makers, exporters, producers, importing countries and future researchers.

The data used in the study were secondary data from the website of Bureau of Agricultural Statistics (BAS) of Philippines Statistics Authority (PSA). Model was selected using time series analysis. Three models were formulated in this study. To select the best model that would forecast the banana production of Philippines from 2015 to 2020, the following criteria were considered: R- Squared; Adjusted R-Squared; standard error of regression; sum of squared residual; Durbin-Watson statistics; Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC).

Forecasted values from the year 2015 to 2020 were computed using the formulated model IMA (0, 2, l) with the equation:
A 2 1nyt = 0.361918A2 1net-1 + 4.276164A 2 1net-2 — 1.638082A 2 1net

The forecasted banana production in the Philippines for the year 2015 to 2020 followed a consistent trend. For the year 2015, the banana production was approximate by be metric tons; 9,289,968.7 metric tons in 2016; 9,543,518.6 metric tons in 2017; 9,796,375.8 metric tons in 2018; 10,049,232.9 metric tons in 2019; and 10,302,090 metric tons in 2020.

The forecast accuracy was also tested using Mean Absolute Percentage Error (MAPE) method and Theil 's U Statistics. The MAPE results showed that there was a small standard error, the computed Theil's U statistics was less than value 1. This was an indication that the forecast was reliable.

Submitted copy to the University Library. 02/14/2017 T-6204