Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches
Outliers are unusual observations that appear in a piece of data that are very different from the rest of the data. The presence of an outlier may directly affect the variance, the model parameters, and the overall estimation, especially during forecasting. To obtain an accurate forecast, any...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English English English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/1095/1/24p%20NORSORAYA%20AZURIN%20WAHIR.pdf http://eprints.uthm.edu.my/1095/2/NORSORAYA%20AZURIN%20WAHIR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1095/3/NORSORAYA%20AZURIN%20WAHIR%20WATERMARK.pdf |
| _version_ | 1796868399101378560 |
|---|---|
| author | Wahir, Norsoraya Azurin |
| author_facet | Wahir, Norsoraya Azurin |
| author_sort | Wahir, Norsoraya Azurin |
| collection | UTHM |
| description | Outliers are unusual observations that appear in a piece of data that are very different
from the rest of the data. The presence of an outlier may directly affect the variance,
the model parameters, and the overall estimation, especially during forecasting. To
obtain an accurate forecast, any outliers that are present in the data must be addressed.
This research used monthly Malaysia tourist arrivals from 1998 until 2015 and an
ARIMA outlier detection method to detect outliers on original data. The detected
outliers were regarded as missing values then treated using interpolation method which
are Linear Interpolation and Cubic Spline Interpolation methods. In this study,
SARIMA model and Artificial Neural Network model were used as forecasting tools
using the data before and after outlier treatment. The comparison of forecast
performance between all models were calculated using MSE, MAD, MAPE and R2
including the data before and after outlier treatment. This study found that once the
outlier in the data was treated, ANN model of Cubic Spline Interpolation performs the
best models compare to other models which is 95.65% using R2
validation test. On the
other hand, ANN approach outperforms SARIMA approach on both data for before
and after outlier treatment which are 6.05% and 2.52%. |
| first_indexed | 2024-03-05T21:39:11Z |
| format | Thesis |
| id | uthm.eprints-1095 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English English English |
| last_indexed | 2024-03-05T21:39:11Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | uthm.eprints-10952021-09-21T06:14:08Z http://eprints.uthm.edu.my/1095/ Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches Wahir, Norsoraya Azurin HD28-70 Management. Industrial Management Outliers are unusual observations that appear in a piece of data that are very different from the rest of the data. The presence of an outlier may directly affect the variance, the model parameters, and the overall estimation, especially during forecasting. To obtain an accurate forecast, any outliers that are present in the data must be addressed. This research used monthly Malaysia tourist arrivals from 1998 until 2015 and an ARIMA outlier detection method to detect outliers on original data. The detected outliers were regarded as missing values then treated using interpolation method which are Linear Interpolation and Cubic Spline Interpolation methods. In this study, SARIMA model and Artificial Neural Network model were used as forecasting tools using the data before and after outlier treatment. The comparison of forecast performance between all models were calculated using MSE, MAD, MAPE and R2 including the data before and after outlier treatment. This study found that once the outlier in the data was treated, ANN model of Cubic Spline Interpolation performs the best models compare to other models which is 95.65% using R2 validation test. On the other hand, ANN approach outperforms SARIMA approach on both data for before and after outlier treatment which are 6.05% and 2.52%. 2020 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/1095/1/24p%20NORSORAYA%20AZURIN%20WAHIR.pdf text en http://eprints.uthm.edu.my/1095/2/NORSORAYA%20AZURIN%20WAHIR%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/1095/3/NORSORAYA%20AZURIN%20WAHIR%20WATERMARK.pdf Wahir, Norsoraya Azurin (2020) Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches. Masters thesis, Universiti Tun Hussein Onn Malaysia. |
| spellingShingle | HD28-70 Management. Industrial Management Wahir, Norsoraya Azurin Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches |
| title | Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches |
| title_full | Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches |
| title_fullStr | Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches |
| title_full_unstemmed | Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches |
| title_short | Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches |
| title_sort | outlier treatments using interolation on malaysia tourist arrival forecasting sarima and ann approaches |
| topic | HD28-70 Management. Industrial Management |
| url | http://eprints.uthm.edu.my/1095/1/24p%20NORSORAYA%20AZURIN%20WAHIR.pdf http://eprints.uthm.edu.my/1095/2/NORSORAYA%20AZURIN%20WAHIR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1095/3/NORSORAYA%20AZURIN%20WAHIR%20WATERMARK.pdf |
| work_keys_str_mv | AT wahirnorsorayaazurin outliertreatmentsusinginterolationonmalaysiatouristarrivalforecastingsarimaandannapproaches |