scholarly journals Peramalan Tingkat Penghunian Tempat Tidur Hotel Bintang Tiga Kota Surakarta Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA)

2019 ◽  
Vol 2 (1) ◽  
pp. 53
Author(s):  
Shindy Dwi Pratiwi
Keyword(s):  
Moving Average ◽  
Hotel Industry ◽  
Occupancy Rate ◽  
Autoregressive Integrated Moving Average ◽  
Occupancy Rates ◽  
The City

<p>Surakarta is a cultural city that is now starting to attract domestic and foreign tourists. This makes many tourists visit the city of Surakarta so that it affects the occupancy rate of hotels in Surakarta. The occupancy rate of hotels in Surakarta has fluctuations from each year. The uncertainty of hotel occupancy rates in Surakarta will certainly affect investors to choose policies in the hotel industry so that hotel occupancy rates in Surakarta City need to be estimated for the next year. In this study, the Autoregressive Integrated Moving Average (ARIMA) method was used to forecast hotel occupancy rates in Surakarta from January to May 2018. By using the best model IMA (1.1), it was concluded that the occupancy rate of three-star Surakarta hotels increased every the month.</p><p><strong>Keywords</strong><strong> : </strong>occupancy rate of hotel, forecasting, ARIMA.</p>

scholarly journals Will Distance to the Capital City Matter When Supplying New Cities in Egypt?

GeoScape ◽  
2016 ◽  
Vol 10 (2) ◽  
pp. 35-52 ◽  
Author(s):  
Mohamed R. Ibrahim ◽  
Houshmand E. Masoumi
Keyword(s):  
Developing Countries ◽  
Urban Areas ◽  
Capital City ◽  
High Rate ◽  
Occupancy Rate ◽  
Spatial Characteristics ◽  
Greater Cairo ◽  
Occupancy Rates ◽  
The City ◽  
Geographical Locations

Abstract Unlike other developing countries, the housing market in Egypt is characterized by densely populated urban areas in old cities and the peripheral urban agglomeration. In contrast, a high rate of vacancy along most of the new cities that have been established since the 1980s is seen. Regardless of such high rate of vacancies, still the variation in occupancy rates among those new cities is notable. Questions arising include: Does proximity to old cities or Greater Cairo affect the size of the population of the new cities? Is the size of the city or the year of establishment plays roles in attracting more inhabitants? The factors of spatial characteristics of new cities in Egypt remain questionable. This research aims to reveal the association between occupancy rate and six factors related to the spatial characteristics of new cities and their geographical locations, such as; current inhabitants, the estimated size of the target group, the size of new cities, total number of housing units, distance to nearby old city, and distance to Greater Cairo.

scholarly journals The Influence of Room Prices and Promotion Costs on Hotel Room Occupancy Rates in Palopo City, South Sulawesi

2020 ◽  
Vol 6 (2) ◽  
pp. 123
Author(s):  
Wayan Suardana ◽  
Muhadjir Suni ◽  
Masri Ridwan
Keyword(s):  
Quantitative Research ◽  
Occupancy Rate ◽  
Hotel Room ◽  
Independent Variables ◽  
South Sulawesi ◽  
The Cost ◽  
Occupancy Rates ◽  
The City ◽  
Positive Effect ◽  
Two Independent Variables

Hotel competition in South Sulawesi Province in 2018 has increased. This study aims to: determine the price of hotel rooms in the city of Palopo, South Sulawesi Province, find out how the cost of hotel promotions in the City of Palopo, South Sulawesi Province, find out whether the room price and promotion costs have a significantly positive effect on hotel room occupancy rates in Palopo City, South Sulawesi Province. This type of research is descriptive quantitative research. Data collection techniques used are the method of observation, documentation techniques and questionnaires. The results showed that of 17 Hotels / Pensions / Villas in Palopo City, South Sulawesi Province, the room occupancy rate was on average 27%. Promotion fee is Rp. 17,368, - per room. Of the two regression coefficients possessed by the two independent variables, the two independent variables (X1) namely room price and promotion costs (X2) have a significant positive effect on the occupancy rate of the room (Y).

scholarly journals How does AirBnb affect local Spanish tourism markets?

2021 ◽  
Author(s):  
Juan Luis Jiménez ◽  
Armando Ortuño ◽  
Jorge V. Pérez-Rodríguez
Keyword(s):  
Data Model ◽  
Positive Relationship ◽  
Dynamic Panel Data ◽  
Occupancy Rate ◽  
Dynamic Panel ◽  
Large Cities ◽  
Dynamic Panel Data Model ◽  
Occupancy Rates ◽  
Overnight Stays ◽  
The City

AbstractThis paper analyses the effects of AirBnb on the size of local tourism markets using AirBnb occupancy rates and hotel overnight stays in order to explore the causal relationship in several Spanish cities. A dynamic panel data model is applied at the city level (2014–2017). Our findings show a positive relationship between the increase in the number of properties offered on AirBnb and the implicit volume of tourists received by each city, specifically in two large cities (Madrid and Barcelona), due to higher AirBnb occupancy rate.

Forecasting disaggregated tourist arrivals in Croatia

2016 ◽  
Vol 23 (1) ◽  
pp. 78-98 ◽  
Author(s):  
Nicholas Apergis ◽  
Andrea Mervar ◽  
James E. Payne
Keyword(s):  
Fourier Transformation ◽  
Moving Average ◽  
The Other ◽  
Autoregressive Moving Average ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Models ◽  
Seasonal Time Series ◽  
Out Of Sample ◽  
The City

This study examines the performance of four alternative univariate seasonal time series forecasting models (seasonal autoregressive integrated moving average [SARIMA], SARIMA with Fourier transformation, ARAR, and fractionally integrated autoregressive-moving average) of tourist arrivals to 20 Croatian counties and the City of Zagreb. Both in-sample and out-of-sample forecasts reveal that the SARIMA model with Fourier transformation consistently outperforms the other models across the respective regions investigated.

Time Series Analysis of Urban Forest Waste in João Pessoa (Northeast Brazil)

2020 ◽  
Vol 46 (3) ◽  
pp. 163-173
Author(s):  
Yuri Rommel Vieira Araújo ◽  
Thiago Freire Melquíades ◽  
Monica Carvalho ◽  
Luiz Moreira Coelho Jr.
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Urban Forest ◽  
Moving Average ◽  
Future Orientation ◽  
Forecast Model ◽  
Northeast Brazil ◽  
Autoregressive Integrated Moving Average ◽  
Forest Waste ◽  
The City

Urban afforestation requires management to ensure its sustainability within the city, and urban pruning waste is generated regularly throughout the year. This paper analyzed the time series of the urban pruning waste volume for João Pessoa (Northeast Brazil) from January 2008 to December 2014, with the objective of determining the volume of urban pruning waste generated and adjusting it to a forecast model. The models studied were part of the ARIMA (Autoregressive Integrated Moving Average) Family. The main results indicated that the ARIMA family models presented satisfactory results for the forecast, and ARIMA (0,1,4) was the model that provided the best forecast for 2014. This study contributes with a better understanding of the pattern and amount of urban pruning waste generated in João Pessoa and could assist the future orientation of municipal public policies.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average)

2014 ◽  
Vol 14 (2) ◽  
pp. 60
Author(s):  
Greis S Lilipaly ◽  
Djoni Hatidja ◽  
John S Kekenusa
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Secondary Data ◽  
Research Use ◽  
Autoregressive Integrated Moving Average ◽  
Minimum Data ◽  
Research Show

PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average) Greis S. Lilipaly1) , Djoni Hatidja1) , John S. Kekenusa1) ABSTRAK Metode ARIMA adalah salah satu metode yang dapat digunakan dalam memprediksi perubahan harga saham. Tujuan dari penelitian ini adalah untuk membuat model ARIMA dan memprediksi harga saham PT. BRI, Tbk. bulan November 2014. Penelitian menggunakan data harga saham  harian  maksimum dan minimum PT. BRI, Tbk. Data yang digunakan yaitu data sekunder yang diambil dari website perusahaan PT. BRI, Tbk. sejak 3 Januari 2011 sampai 20 Oktober 2014 untuk memprediksi harga saham bulan November 2014. Dari hasil penelitian menunjukkan bahwa data tahun 2011 sampai Oktober 2014 bisa digunakan untuk memprediksi harga saham bulan November 2014. Hasilnya model ARIMA untuk harga saham maksimum adalah ARIMA (2,1,3) dan harga saham minimum adalah model (2,1,3) yang dapat digunakan untuk memprediksi data bulan November 2014 dengan validasi prediksi yang diambil pada bulan Oktober 2014 untuk selanjutnya dilakukan prediksi bulan November 2014. Kata Kunci: Metode ARIMA, PT. BRI, Tbk., Saham THE PREDICTION STOCK PRICE OF PT. BRI, Tbk. USE ARIMA METHOD (Autoregressive Integrated Moving Average) ABSTRACT ARIMA method is one of the method that used to prediction the change of stock price. The purpose of this research is to make model of ARIMA and predict stock price of PT. BRI, Tbk. in November 2014. The research use maximum and minimum data of stock price daily of PT. BRI, Tbk. Data are used is secondary data that taking from website of PT. BRI, Tbk. since January 3rd 2011 until October 20th 2014 to predict stock price in November 2014. From this research show that data from 2011 until October 2014 can be used to predict the stock price in November 2014. The result of ARIMA’s model for the maximum stock price is ARIMA (2,1,3) and the minimum stock price is (2,1,3) can use to predict the data on November 2014 with predict validation that take on October 2014 and with that predict November 2014. Keywords: ARIMA method, PT. BRI, Tbk., Stock

Sign in / Sign up

Export Citation Format

Share Document