Hamsyah, Ilham Fauzi (2015) Perbandingan GSTAR Dan ARIMA Filter Kalman Dalam Perbaikan Hasil Prediksi Debit Air Sungai Brantas. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
ARIMA Box-jenkins adalah salah satu metode time series yang
biasa digunakan untuk melakukan analisis data dan peramalan.
Dalam kehidupan sehari-hari, kita sering menemukan data yang
mempunyai keterkaitan antar waktu dan keterkaitan antar lokasi.
Data seperti ini disebut data spasial. Debit air Sungai
mempunyai keadaan yang heterogen pada setiap waktu dan
lokasi pengukuran yang dipengaruhi sifat acak alam, sehingga
karakteristik debit air disetiap lokasi berbeda. Untuk
mendapatkan prediksi yang mempunyai tingkat error yang kecil,
maka akan dilakukan perbandingan dua model yaitu model
Generalized Space Time Autoregressive (GSTAR) dan model
Autoregressive Integrated Moving Average (ARIMA) Filter
Kalman. Algoritma Filter Kalman akan diterapkan pada hasil
ramalan Pemodelan ARIMA dengan pengambilan derajat
polinomial kesatu, dua, dan tiga untuk memperbaiki prediksi 14
hari ke depan. Hasil akhir menujukan bahwa Filter Kalman
mampu memperbaiki hasil ARIMA dan mempunyai tingkat error
yang lebih kecil dibandingkan dengan GSTAR(31) inverse jarak,
yang ditunjukan melalui hasil simulasi berupa grafik dan
diperjelas dengan nilai MAPE yang lebih kecil.
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ARIMA Box-Jenkins is one method of time series which is used to
perform data analysis and forecasting. In daily life, we often find
data that have a relation between the time and relation between
locations. Data such as these are called spatial data. River water
discharge have a heterogeneous situation at any time and
location measurements influenced the random of nature, so that
the water discharge characteristics at each location. To get the
predictions that has a small error rate, it will be the comparison
of two models, namely models Generalized Space Time
Autoregressive (GSTAR) and models Autoregressive Integrated
Moving Average (ARIMA) Kalman Filter. Kalman Filter
algorithm will be applied to the results forecast by the ARIMA
modeling decision-degree polynomial 1-st, 2-nd, and 3-rd to improve
the prediction of the next 14 days. The final result vector that is
able to improve the results of ARIMA Kalman Filter and have a
level of error that is smaller than the GSTAR (31) inverse
distance, which is demonstrated through simulation results in the
form of graphs and clarified with a smaller MAPE value.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSMa 519.536 Ham p |
| Uncontrolled Keywords: | ARIMA, ARIMA Filter Kalman, polinomial derajat, GSTAR |
| Subjects: | H Social Sciences > HA Statistics |
| Divisions: | Faculty of Mathematics and Science > Mathematics > 44201-(S1) Undergraduate Thesis |
| Depositing User: | Yeni Anita Gonti |
| Date Deposited: | 10 Feb 2020 07:14 |
| Last Modified: | 10 Feb 2020 07:18 |
| URI: | http://repository.its.ac.id/id/eprint/74788 |
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