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Relevant bibliographies by topics / ARIMA modely

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Journal articles

Academic literature on the topic 'ARIMA modely'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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Journal articles on the topic "ARIMA modely"

1

ALKALI, MUSA ABUBAKAR. "ASSESSING THE FORECASTING PERFORMANCE OF ARIMA AND ARIMAX MODELS OF RESIDENTIAL PRICES IN ABUJA NIGERIA." Asia Proceedings of Social Sciences 4, no. 1 (2019): 4–6. http://dx.doi.org/10.31580/apss.v4i1.528.

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This paper compared the out of sample forecasting ability of two Box-Jenkins ARIMA family models: ARIMAX and ARIMA. The forecasting models were tested to forecast real estate residential price in Abuja, Nigeria with quarterly data of average sales of residential price from the first quarter of year 2000 to the last quarter of year 2017. The result shows that the ARIMAX forecasting models, with macroeconomic factors as exogenous variables such as the household income, interest rate, gross domestic products, exchange rate and crude oil price and their lags, provide the best out of sample forecas

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2

Pektaş, Ali Osman, and H. Kerem Cigizoglu. "ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient." Journal of Hydrology 500 (September 2013): 21–36. http://dx.doi.org/10.1016/j.jhydrol.2013.07.020.

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3

Marriott, John, and Paul Newbold. "Bayesian Comparison of ARIMA and Stationary ARMA Models." International Statistical Review / Revue Internationale de Statistique 66, no. 3 (1998): 323. http://dx.doi.org/10.2307/1403520.

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Marriott, John, and Paul Newbold. "Bayesian Comparison of ARIMA and Stationary ARMA Models." International Statistical Review 66, no. 3 (1998): 323–36. http://dx.doi.org/10.1111/j.1751-5823.1998.tb00376.x.

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5

Adekanmbi et al.,, Adekanmbi et al ,. "ARIMA and ARIMAX Stochastic Models for Fertility in Nigeria." International Journal of Mathematics and Computer Applications Research 7, no. 5 (2017): 1–20. http://dx.doi.org/10.24247/ijmcaroct20171.

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6

Lima, Ricardo Chaves, Marcos Roberto Góis, and Charles Ulises. "Previsão de preços futuros de Commodities agrícolas com diferenciações inteira e fracionária, e erros heteroscedásticos." Revista de Economia e Sociologia Rural 45, no. 3 (2007): 621–44. http://dx.doi.org/10.1590/s0103-20032007000300004.

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O presente trabalho tem como objetivo modelar séries temporais para efeito de previsão com diferenciações inteira e fracionária, utilizando dados de preços futuros de commodities agrícolas. Modelos de séries temporais do tipo ARMA/ARIMA (diferenciação inteira) serão estimados como termo de comparação com os modelos do tipo ARFIMA (diferenciação fracionária). Em ambos os casos, os erros dos modelos serão estimados assumindo-se a possibilidade de estimação da volatilidade. O poder de previsão de cada modelo será comparado pelo critério do erro quadrado médio da previsão (EQM). A estimação do ter

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7

Kurnia, Alma, and Ibnu Hadi. "Peramalan Nilai Ekspor Produk Industri Alas Kaki Menggnakan Model ARIMAX dengan Efek Variasi Kalender." Jurnal Statistika dan Aplikasinya 3, no. 2 (2019): 25–34. http://dx.doi.org/10.21009/jsa.03204.

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Model ARIMAX adalah model ARIMA dengan peubah tambahan. Peubah tambahan yang digunakan untuk data deret waktu dengan variasi kalender berupa variabel dummy. Pada makalah ini, akan dilakukan penghitungan peramalan nilai ekspor produk industri alas kaki bulan Juli 2019 sampai dengan Jui 2020 dengan menggunakan model ARIMAX dengan efek variasi kalender. Efek variasi kalender yang ditemukan pada data nilai ekspor produk industri alas kaki adalah libur hari raya Idul Fitri. Data yang digunakan pada makalah ini yaitu data nilai ekspor produk industri alas kaki mulai dari bulan Januari tahun 2010 sam

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8

Zhang, Manfei, Yimeng Wang, Xiao Wang, and Weibo Zhou. "Groundwater Depth Forecasting Using a Coupled Model." Discrete Dynamics in Nature and Society 2021 (February 24, 2021): 1–11. http://dx.doi.org/10.1155/2021/6614195.

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Accurate and reliable prediction of groundwater depth is a critical component in water resources management. In this paper, a new method based on coupling wavelet decomposition method (WA), autoregressive moving average (ARMA) model, and BP neural network (BP) model for groundwater depth forecasting applications was proposed. The relative performance of the proposed coupled model (WA-ARMA-BP) was compared to the regular autoregressive integrated moving average (ARIMA) and BP models for annual average groundwater depth forecasting using leave-one-out cross-validation (LOO-CV). The variables use

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9

Putera, Muhammad Luthfi Setiarno. "IMPROVISASI MODEL ARIMAX-ANFIS DENGAN VARIASI KALENDER UNTUK PREDIKSI TOTAL TRANSAKSI NON-TUNAI." Indonesian Journal of Statistics and Its Applications 4, no. 2 (2020): 296–310. http://dx.doi.org/10.29244/ijsa.v4i2.603.

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Developed information technology boosts interest to use non-cash payment media in many areas. Following the high usage of a non-cash scheme in many payment transactions recently, the objective of this work is two-fold that is to predict the total of a non-cash transaction by using various time-series models and to compare the forecasting accuracy of those models. As a country with a mostly dense Moslem population, plenty of economical activities are arguably influenced by the Islamic calendar effect. Therefore the models being compared are ARIMA, ARIMA with Exogenous (ARIMAX), and a hybrid bet

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10

Wang, S., L. L. Liu, L. K. Huang, Y. Z. Yang, and H. Peng. "PERFORMANCE EVALUATION OF IONOSPHERIC TEC FORECASTING MODELS USING GPS OBSERVATIONS AT DIFFERENT LATITUDES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 1175–82. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-1175-2020.

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Abstract. In this paper, Holt-Winters model, ARMA model and ARIMA model in time series analysis were used to predict total electron content (TEC). Taking ionospheric grid data of quiet period and active period in different longitude and latitude provided by IGS center as sample data, the TEC data of the first 8 days were used to build four kinds of prediction models and forecast TEC values of the next 6 days, and the results were compared with the observations provided by IGS center. The prediction effects of the four models in different ionospheric environments and different longitude and lat

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