An analysis and forecast of LRT demand using Arima models

Date of Publication

1996

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Mathematics

College

College of Science

Department/Unit

Mathematics and Statistics

Abstract/Summary

This thesis is about forecasting LRT demand using the Univariate Box-Jenkins ARIMA models. It is a requirement in forecasting that the data must be stationary. Nonstationary data can be converted into a stationary data by differencing. There are four common processes used in forecasting these are Autoregressive (AR), Moving Average (MA), Mixed (ARIMA) processes. There are three stages in obtaining an appropriate model before forecasting: (1) Identification, (2) Estimation, and (3) Diagnostic checking. Using the monthly LRT ridership starting from December 1984 to September 1996 as our data, a final model (an integrated mixed ARIMA model) was used to forecast the LRT demand beyond the period covered by the data used in this research. And with the final model, the researchers was able to forecast the LRT demand for the next 36 observations or three years from the last observation date.

Abstract Format

html

Language

English

Format

Print

Accession Number

TU07664

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

129 leaves

Keywords

Mathematical models; Computer programs; Transportation; Light Rail Transit (LRT); Time-series analysis

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