CD Skripsi

Peramalan Curah Hujan Bulanan Dengan Tipe Iklim Berdasarkan Klasifikasi Iklim Koppen Dengan Metode Prophet Dan Nnetar

---

A time series is defined as a collection of quantitative observations arranged chronologically. Proper understanding of time series is a major problem in various fields. Currently, models have been developed that are quite good for making predictions. Among them are prophet and Neural Network Autoregressive (NNETAR). Prophet is a time series model developed by Sean J. Taylor and Benjamin Letham who are a data science team from Facebook in 2017. The basis of this model is a decomposable time series with 3 model components, namely trend, seasonality, and vacation. Meanwhile, Autoregressive Neural Network or usually abbreviated as NNETAR is a time series model based on machine learning, namely using an Artificial Neural Network (ANN) with input lag in time series data. This research aims to see the performance of the prophet and NNETAR models in rainfall prediction data. The results of this study show that the prophet model is better able to predict seasonal patterns of rainfall than the NNETAR model. Where the average MASE of the prophet model is 0.68 while the NNETAR model has an average MASE of 0.87.
Keywords: Neural network auto regressive (NNETAR), prophet, time series

---

Ketersediaan

Informasi Detail

Judul Seri

-

No. Panggil

2103113387

Penerbit

Pekanbaru : Universitas Riau FMIPA Statistika., 2025

Deskripsi Fisik

-

Bahasa

Indonesia

ISBN/ISSN

-

Klasifikasi

2103113387

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

STATISTIKA

Info Detail Spesifik

-

Pernyataan Tanggungjawab

Mutia

Versi lain/terkait

Lampiran Berkas

Komentar

---

Anda harus masuk sebelum memberikan komentar