Employing long short-term memory and Facebook prophet model in air temperature forecasting

One of information needed in weather forecast is air temperature. This value might change any time. Prediction of air temperature is very valuable for some communities and occasions. Therefore, high accuracy prediction is needed. Since the information about air temperature might vary over time, it i...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Communications in statistics. Simulation and computation Ročník 52; číslo 2; s. 279 - 290
Hlavní autoři: Toharudin, Toni, Pontoh, Resa Septiani, Caraka, Rezzy Eko, Zahroh, Solichatus, Lee, Youngjo, Chen, Rung Ching
Médium: Journal Article
Jazyk:angličtina
Vydáno: Philadelphia Taylor & Francis 01.02.2023
Taylor & Francis Ltd
Témata:
Air temperature
Big Data
Forecasting
LSTM
Machine learning
Mathematical models
Missing data
Neural networks
Outliers (statistics)
Prophet model
Short term
Social networks
Temperature
Weather forecasting
ISSN:0361-0918, 1532-4141
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:One of information needed in weather forecast is air temperature. This value might change any time. Prediction of air temperature is very valuable for some communities and occasions. Therefore, high accuracy prediction is needed. Since the information about air temperature might vary over time, it is necessary to implement methods that can adapt to this situation. The use of neural network methods such as long short term memory (LSTM), nowadays, becomes popular in facing big data including unexpected fluctuation on the data. Thus, the model is used in this paper which provides long series data on air temperature. In addition, recently, Facebook announced an accurate method of forecasting, called Prophet model's, for data which have trend, seasonality, holidays, missing data, not to mention outliers. Hence, the forecast of five-year daily air temperatures in Bandung on this paper is modeled by LSTM and Facebook Prophet. The result shows that, for minimum temperature, Prophet performs better on maximum air temperature while LSTM performs better on minimum air temperature. However, the difference on the value of RMSE is not too large significant.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2020.1854302