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Journal articles on the topic 'ARIMA modely'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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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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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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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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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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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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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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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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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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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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Musa, Mohammed Ibrahim. "Malaria Disease Distribution in Sudan Using Time Series ARIMA Model." International Journal of Public Health Science (IJPHS) 4, no. 1 (2015): 7. http://dx.doi.org/10.11591/ijphs.v4i1.4705.

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<p>Malaria is widely spread and distributed in the tropical and subtropical regions of the world. Sudan is a sub-Saharan African country that is highly affected by malaria with 7.5 million cases and 35,000 deaths every year. The auto-regressive integrated moving average (ARIMA) model was used to predict the spread of malaria in the Sudan. The ARIMA model used malaria cases from 2006 to 2011 as a training set, and data from 2012 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2013 and 2014. The ARIMAX model was carried out to examine th

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Musa, Mohammed Ibrahim. "Malaria Disease Distribution in Sudan Using Time Series ARIMA Model." International Journal of Public Health Science (IJPHS) 4, no. 1 (2015): 7. http://dx.doi.org/10.11591/.v4i1.4705.

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<p>Malaria is widely spread and distributed in the tropical and subtropical regions of the world. Sudan is a sub-Saharan African country that is highly affected by malaria with 7.5 million cases and 35,000 deaths every year. The auto-regressive integrated moving average (ARIMA) model was used to predict the spread of malaria in the Sudan. The ARIMA model used malaria cases from 2006 to 2011 as a training set, and data from 2012 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2013 and 2014. The ARIMAX model was carried out to examine th

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Mahmad Azan, Atiqa Nur Azza, Nur Faizatul Auni Mohd Zulkifly Mototo, and Pauline Jin Wee Mah. "The Comparison between ARIMA and ARFIMA Model to Forecast Kijang Emas (Gold) Prices in Malaysia using MAE, RMSE and MAPE." Journal of Computing Research and Innovation 6, no. 3 (2021): 22–33. http://dx.doi.org/10.24191/jcrinn.v6i3.225.

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Gold is known as the most valuable commodity in the world because it is a universal currency recognized by every single bank across the globe. Thus, many people were interested in investing gold since gold market was always steadier compared to other investment (Khamis and Awang, 2020). However, the credibility of gold was questionable due to the changes in gold prices caused by a variety of circumstances (Henriksen, 2018). Hence, information on the inflation of gold prices were needed to understand the trend in order to plan for the future in accordance with international gold price standards

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Mah, P. J. W., N. A. M. Ihwal, and N. Z. Azizan. "FORECASTING FRESH WATER AND MARINE FISH PRODUCTION IN MALAYSIA USING ARIMA AND ARFIMA MODELS." MALAYSIAN JOURNAL OF COMPUTING 3, no. 2 (2018): 81. http://dx.doi.org/10.24191/mjoc.v3i2.4887.

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Malaysia is surrounded by sea, rivers and lakes which provide natural sources of fish for human consumption. Hence, fish is one source of protein supply to the country and fishery is a sub-sector that contribute to the national gross domestic product. Since fish forecasting is crucial in fisheries management for managers and scientists, time series modelling can be one useful tool. Time series modelling have been used in many fields of studies including the fields of fisheries. In a previous research, the ARIMA and ARFIMA models were used to model marine fish production in Malaysia and the ARF

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Hatta, Hanisah Hanun Muhamad, Faezzah Mohd Daud, and Norsyafiqah Mohamad. "An Application of Time Series ARIMA Forecasting Model for Predicting the Ringgit Malaysia-Dollar Exchange Rate." Journal of Data Analysis 1, no. 1 (2018): 42–48. http://dx.doi.org/10.24815/jda.v1i1.11884.

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ABSTRAK. Model ARIMA yang dilambangkan sebagai ARIMA (p, d, q), pada dasarnya dari Auto Regression Moving Average (ARMA) dengan proses differencing. Objek utama untuk melakukan proses ARIMA adalah memprediksi kinerja masa depan data tertentu, dengan melakukan differencing terhadap data yang jelas atau saat ini. Prediksi dihitung untuk memiliki data yang lebih baik untuk time series berikutnya. Agar memiliki data yang baik dan sempurna, ubah data non-stasioner menjadi data stasioner. Adalah mungkin untuk memiliki lebih dari satu kali proses pembedaan untuk menciptakan model ARIMA terbaik. Tulis

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Maya Sierra, Giuliana, and Nini Johana Marin Rodríguez. "Modelación y comovimientos de la tasa de cambio colombiana, 2011-2017." Revista de Métodos Cuantitativos para la Economía y la Empresa 28 (November 8, 2019): 301–41. http://dx.doi.org/10.46661/revmetodoscuanteconempresa.2966.

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La tasa de cambio está influenciada por múltiples factores macroeconómicos nacionales e internacionales, lo que genera altos niveles de incertidumbre. El objetivo de esta investigación es la construcción de modelos ARIMA-GARCH y ARIMAX-GARCH como herramienta para el pronóstico de la tasa de cambio en Colombia a partir de los retornos diarios de los precios de cierre USD/COP y su análisis de correlación dinámica con algunas variables de interés. Los resultados sugieren que la incorporación de variables exógenas significativas dentro de la modelación ARIMAX-GARCH con correlación persistente segú

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Obi, C. V., and C. N. Okoli. "Comparative Performance of the ARIMA, ARIMAX and SES Model for Estimating Reported Cases of Diabetes Mellitus in Anambra State, Nigeria." European Journal of Engineering and Technology Research 6, no. 1 (2021): 63–68. http://dx.doi.org/10.24018/ejers.2021.6.1.2321.

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This study examined the performance of the ARIMA, ARIMAX and the Single Exponential Smoothing (SES) model for the estimation of diabetes cases in Anambra State with the following specific objectives: to fit the model to the data, to determine the best fit model for estimating diabetes mellitus cases and forecast for expected cases for period of five years. The secondary data used for the study is sourced from records of Anambra state Ministry of Health. The Akaike information criterion is adopted for assessing the performance of the models. The R-software is employed for the analysis of data.

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Gautam, Ratnesh, and Anand K. Sinha. "Time series analysis of reference crop evapotranspiration for Bokaro District, Jharkhand, India." Journal of Water and Land Development 30, no. 1 (2016): 51–56. http://dx.doi.org/10.1515/jwld-2016-0021.

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AbstractEvapotranspiration is the one of the major role playing element in water cycle. More accurate measurement and forecasting of Evapotranspiration would enable more efficient water resources management. This study, is therefore, particularly focused on evapotranspiration modelling and forecasting, since forecasting would provide better information for optimal water resources management. There are numerous techniques of evapotranspiration forecasting that include autoregressive (AR) and moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARI

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Derbentsev, Vasily, Natalia Datsenko, Olga Stepanenko, and Vitaly Bezkorovainyi. "Forecasting cryptocurrency prices time series using machine learning approach." SHS Web of Conferences 65 (2019): 02001. http://dx.doi.org/10.1051/shsconf/20196502001.

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This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate th

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Kannan, K. Senthamarai, and E. Sakthivel E. Sakthivel. "Fuzzy Time Series Model and ARIMA Model - A Comparative Study." Indian Journal of Applied Research 4, no. 8 (2011): 624–36. http://dx.doi.org/10.15373/2249555x/august2014/166.

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Mah, Pauline Jin Wee, and Nur Nadhirah Nanyan. "A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA." MALAYSIAN JOURNAL OF COMPUTING 5, no. 1 (2020): 374. http://dx.doi.org/10.24191/mjoc.v5i1.6760.

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The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressi

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Duppati, Geeta, Anoop S. Kumar, Frank Scrimgeour, and Leon Li. "Long memory volatility in Asian stock markets." Pacific Accounting Review 29, no. 3 (2017): 423–42. http://dx.doi.org/10.1108/par-02-2016-0009.

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Purpose The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory. Design/methodology/approach This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integ

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Zhuravka, Fedir, Hanna Filatova, and John O. Aiyedogbon. "Government debt forecasting based on the Arima model." Public and Municipal Finance 8, no. 1 (2020): 120–27. http://dx.doi.org/10.21511/pmf.08(1).2019.11.

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The paper explores theoretical and practical aspects of forecasting the government debt in Ukraine. A visual analysis of changes in the amount of government debt was conducted, which has made it possible to conclude about the deepening of the debt crisis in the country. The autoregressive integrated moving average (ARIMA) is considered as the basic forecasting model; besides, the model work and its diagnostics are estimated. The EViews software package illustrates the procedure for forecasting the Ukrainian government debt for the ARIMA model: the series for stationarity was tested, the time s

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Zhang, Xiuzhen, Zhiping Lu, Yangye Wang, and Riquan Zhang. "Adjusted jackknife empirical likelihood for stationary ARMA and ARFIMA models." Statistics & Probability Letters 165 (October 2020): 108830. http://dx.doi.org/10.1016/j.spl.2020.108830.

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Zhang, Rui, Zhen Guo, Yujie Meng, et al. "Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China." International Journal of Environmental Research and Public Health 18, no. 11 (2021): 6174. http://dx.doi.org/10.3390/ijerph18116174.

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Background: This study intends to identify the best model for predicting the incidence of hand, foot and mouth disease (HFMD) in Ningbo by comparing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) models combined and uncombined with exogenous meteorological variables. Methods: The data of daily HFMD incidence in Ningbo from January 2014 to November 2017 were set as the training set, and the data of December 2017 were set as the test set. ARIMA and LSTM models combined and uncombined with exogenous meteorological variables were adopted to fit th

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Zhang, Xiaofan, Chao Liu, and Yuhang Qian. "Coal Price Forecast Based on ARIMA Model." Financial Forum 9, no. 4 (2021): 180. http://dx.doi.org/10.18282/ff.v9i4.1530.

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<div>This paper analyzes and determines the decision variables and constraints, establishes the EECM-ARAMA model to analyze and research coal price forecasts. Firstly, we first confirm the influencing factors. Then, we conduct correlation coefficient tests on price and various factors, and get the strength of the correlation between each factor and price. The second is to establish a coal price prediction model. Firstly, we use the EEMD method to transform the original price series into a stable time series, and then formulate three ARIMA models by comparing the size of the influencing f

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GRZELAK, Małgorzata. "APPLICATION OF ARIMA MODEL FOR FORECASTING PRODUCTION QUANTITY IN ENTERPRISE." Systemy Logistyczne Wojsk 50, no. 1 (2019): 93–106. http://dx.doi.org/10.37055/slw/129234.

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Celem przedsiębiorstw produkcyjnych jest zaspokajanie potrzeb klientów, poprzez terminowe wytwarzanie wyrobów zgodnie z popytem występującym na rynku. Powyższe działania umożliwiane są przez prawidłowe sporządzanie prognoz potencjalnych zamówień. W poniższym artykule przedstawiono model ARIMA jako narzędzie wspierające planowanie wielkości produkcji w przedsiębiorstwie. Dokonano również oceny wiarygodności opracowanego modelu poprzez analizę reszt oraz ich autokorelacji i autokorelacji cząstkowych.

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Kumar, Anoop. "Testing for long memory in volatility in the Indian Forex market." Ekonomski anali 59, no. 203 (2014): 75–90. http://dx.doi.org/10.2298/eka1403075k.

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This article attempts to verify the presence of long memory in volatility in the Indian foreign exchange market using daily bilateral returns of the Indian Rupee against the US dollar from 17/02/1994 to 08/11/2013. In the first part of the analysis the presence of long-term dependence is confirmed in the return series as well as in two measures of unconditional volatility (absolute returns and squared returns) by employing three measures of long memory. Next, the presence of long memory in conditional volatility is tested using ARMA-FIGARCH and ARMA-FIAPARCH models under various distributional

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N. N. Jambhulkar, N. N. Jambhulkar. "Modeling of Rice Production in Punjab using ARIMA Model." International Journal of Scientific Research 2, no. 8 (2012): 1–2. http://dx.doi.org/10.15373/22778179/aug2013/1.

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Trevisan, Elma Suema, Reinaldo Castro Souza, and Leonardo Rocha Souza. "Estimação do parâmetro "d " em modelos arfima." Pesquisa Operacional 20, no. 1 (2000): 73–82. http://dx.doi.org/10.1590/s0101-74382000000100008.

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Os modelos ARFIMA caracterizam-se por sua longa dependência e por possuírem o parâmetro d do modelo ARIMA (grau de diferenciação) assumindo valores fracionários. Quando no caso d <FONT FACE=Symbol>Î</FONT> (-0,5; 0,5), há estacionariedade. A longa dependência aparece quando d é positivo. Este trabalho visa testar e comparar duas metodologias para o processo de estimação de d, baseadas na função Periodograma e na função Periodograma Suavizado. Através de séries sintéticas geradas para este fim, foram realizadas simulações em quatro diferentes estruturas ARFIMA, a saber : (0,d,0), (1

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Çatık, A. Nazif, and Mehmet Karaçuka. "A COMPARATIVE ANALYSIS OF ALTERNATIVE UNIVARIATE TIME SERIES MODELS IN FORECASTING TURKISH INFLATION." Journal of Business Economics and Management 13, no. 2 (2012): 275–93. http://dx.doi.org/10.3846/16111699.2011.620135.

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This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at earlier forecast horizons conventional models, especially ARFIMA and ARIMA, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer in terms of dynamic forecasts. The superiority of the unobserved components model suggests that inflatio

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Jadevicius, Arvydas, and Simon Huston. "Property market modelling and forecasting: simple vs complex models." Journal of Property Investment & Finance 33, no. 4 (2015): 337–61. http://dx.doi.org/10.1108/jpif-08-2014-0053.

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Purpose – The commercial property market is complex, but the literature suggests that simple models can forecast it. To confirm the claim, the purpose of this paper is to assess a set of models to forecast UK commercial property market. Design/methodology/approach – The employs five modelling techniques, including Autoregressive Integrated Moving Average (ARIMA), ARIMA with a vector of an explanatory variable(s) (ARIMAX), Simple Regression (SR), Multiple Regression, and Vector Autoregression (VAR) to model IPD UK All Property Rents Index. The Bank Rate, Construction Orders, Employment, Expendi

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Graf, Renata, and Pouya Aghelpour. "Daily River Water Temperature Prediction: A Comparison between Neural Network and Stochastic Techniques." Atmosphere 12, no. 9 (2021): 1154. http://dx.doi.org/10.3390/atmos12091154.

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The temperature of river water (TRW) is an important factor in river ecosystem predictions. This study aims to compare two different types of numerical model for predicting daily TRW in the Warta River basin in Poland. The implemented models were of the stochastic type—Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA)—and the artificial intelligence (AI) type—Adaptive Neuro Fuzzy Inference System (ANFIS), Radial Basis Function (RBF) and Group Method of Data Handling (GMDH). The ANFIS and RBF models had the most f

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Dawoud, Issam, and Selahattin Kaçiranlar. "An optimal k of kth MA-ARIMA models under a class of ARIMA model." Communications in Statistics - Theory and Methods 46, no. 12 (2016): 5754–65. http://dx.doi.org/10.1080/03610926.2015.1112910.

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Reddy, P. Ramakrishna, and B. Sarojamma B. Sarojamma. "Weighted Average ARMA Model." Indian Journal of Applied Research 4, no. 8 (2011): 641–43. http://dx.doi.org/10.15373/2249555x/august2014/168.

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Fouskitakis, G. N., and S. D. Fassois. "Pseudolinear estimation of fractionally integrated ARMA (ARFIMA) models with automotive application." IEEE Transactions on Signal Processing 47, no. 12 (1999): 3365–80. http://dx.doi.org/10.1109/78.806080.

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Matskul, Valerii, Diana Okara, and Nataliia Podvalna. "The Ukraine and EU trade balance: prediction via various models of time series." SHS Web of Conferences 73 (2020): 01020. http://dx.doi.org/10.1051/shsconf/20207301020.

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This article is the first to study, simulate and forecast the monthly dynamics of the trade balance between Ukraine and the European Union for the period from 2005 to 2019. In the presented work, three types of models were used for modeling and forecasting: Automated Neural Networks, additive models ARIMA *ARIMAS (Autoregressive integrated moving average with season component) and Holts model with a damped trend. When modeling using the Automated Neural Networks module, various ensembles of networks and neuron activation functions in hidden layers were used. It turned out that Automated Neural

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Verma, P. "Regression, ARIMA and ARIMAX Models to Study the Factors affecting Foreign Direct Investment in India." Asian Journal of Research in Business Economics and Management 5, no. 12 (2015): 1. http://dx.doi.org/10.5958/2249-7307.2015.00199.1.

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Mbithe Titus, Cecilia, Anthony Wanjoya, and Thomas Mageto. "Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models." International Journal of Data Science and Analysis 7, no. 1 (2021): 1. http://dx.doi.org/10.11648/j.ijdsa.20210701.11.

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Wu, Menglong, Yicheng Ye, Nanyan Hu, Qihu Wang, Huimin Jiang, and Wen Li. "EMD-GM-ARMA Model for Mining Safety Production Situation Prediction." Complexity 2020 (June 8, 2020): 1–14. http://dx.doi.org/10.1155/2020/1341047.

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In order to improve the prediction accuracy of mining safety production situation and remove the difficulty of model selection for nonstationary time series, a grey (GM) autoregressive moving average (ARMA) model based on the empirical mode decomposition (EMD) is proposed. First of all, according to the nonstationary characteristics of the mining safety accident time series, nonstationary original time series were decomposed into high- and low-frequency signals using the EMD algorithm, which represents the overall trend and random disturbances, respectively. Subsequently, the GM model was used

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KOZICKI, Bartosz. "THE IMPLEMENTATION OF ARIMA MODEL FOR THE FORECAST OF IMPORTATION OF GOODS TO POLAND IN 2019." Systemy Logistyczne Wojsk 50, no. 1 (2019): 127–41. http://dx.doi.org/10.37055/slw/129236.

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W artykule poruszony został problem z zakresu analizy i oceny danych dotyczących importu towarów do Polski w latach 2011-2018 w milionach ton oraz próba przeprowadzenia prognozowania eksportu w Polsce na czternaście przyszłych okresów modelem ARIMA. Badania rozpoczęto od analizy i oceny danych dotyczących importu towarów w milionach ton w Polsce w ujęciu dynamicznym. Następnie na podstawie uzyskanych ocen wybrano model prognostyczny ARIMA, a następnie zbudowano dwa modele uczące typu ARIMA. Zbudowane modele ARIMA zostały poddane analizie i ocenie. Wybrano najlepszy. Na jego podstawie wykonano

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Prada-Núñez, Raúl, and Cesar Augusto Hernández-Suárez. "Análisis de una serie de tiempo utilizando diseño de experimentos como herramienta de calibración." Eco matemático 6, no. 1 (2015): 50. http://dx.doi.org/10.22463/17948231.459.

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ResumenLas series temporales se usan para estudiar la relación de una variable consigo misma a lo largo del tiempo en intervalos regulares; se consideró el consumo energético de España durante una muestra de 5 días, recurriendo a diversos modelos deterministas se buscaba modelar su comportamiento de la forma más ajustada. Se utiliza el diseño de experimentos para calibrar los parámetros del modelo de HoltWinters validando aquellos efectos que resultan significativos en la minimización del MAPE, con el fin de identificar las Condiciones Operativas Óptimas del modelo. Por último, se evaluan dive

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Khairunnisa, Sarah, Nusyrotus Sa’dah, Isnani, Rohmah Artika, and Prihantini. "Forecasting and Effectiveness Analysis of Domestic Airplane Passengers in Yogyakarta Adisutjipto Airport with Autoregressive Integrated Moving Average with Exogeneous (ARIMAX) Model." Proceeding International Conference on Science and Engineering 3 (April 30, 2020): 365–69. http://dx.doi.org/10.14421/icse.v3.529.

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Airplane is one of the public transportations options that many people choose when traveling long distance. Nowadays, it is notes that the number of passengers domestic flight has increased from the previous months. This increase, especially occurs on the holidays, such as year-end holidays, Eid, and others. The increase of airplane passengers is inversely proportional to the number of available airplane. Forecasting the number of airplane passangers is necessary to prepare additional facilities when there is increasing passengers. This research focused on forecasting domestic airplane passeng

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Gonçalves Cas, Carlos. "Application of The ARIMA Model to Forecast the Price of the Commodity Corn." Revista Gestão da Produção Operações e Sistema 11, no. 1 (2018): 263–79. http://dx.doi.org/10.15675/gepros.v13i1.2040.

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Hauser, Michael A. "Maximum likelihood estimators for ARMA and ARFIMA models: a Monte Carlo study." Journal of Statistical Planning and Inference 80, no. 1-2 (1999): 229–55. http://dx.doi.org/10.1016/s0378-3758(98)00252-3.

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McLeod, A. Ian. "Necessary and Sufficient Condition for Nonsingular Fisher Information Matrix in ARMA and Fractional ARIMA Models." American Statistician 53, no. 1 (1999): 71. http://dx.doi.org/10.2307/2685656.

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McLeod, A. Ian. "Necessary and Sufficient Condition for Nonsingular Fisher Information Matrix in ARMA and Fractional ARIMA Models." American Statistician 53, no. 1 (1999): 71–72. http://dx.doi.org/10.1080/00031305.1999.10474433.

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Eğri˙oğlu, Erol, and Süleyman Günay. "Bayesian model selection in ARFIMA models." Expert Systems with Applications 37, no. 12 (2010): 8359–64. http://dx.doi.org/10.1016/j.eswa.2010.05.047.

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Ahn, Sanghun, Hoon Kang, Jaehoon Cho, Tae-Ok Kim, and Dongil Shin. "Forecasting Model Design of Fire Occurrences with ARIMA Models." Journal of the Korean Institute of Gas 19, no. 2 (2015): 20–28. http://dx.doi.org/10.7842/kigas.2015.19.2.20.

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Kim, Kwan-Hyung, and Han-Soo Kim. "KTX Passenger Demand Forecast with Intervention ARIMA Model." Journal of the Korean society for railway 14, no. 5 (2011): 470–76. http://dx.doi.org/10.7782/jksr.2011.14.5.470.

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