Journal articles: 'Arimaa'

1

Syed, Omar, and Jeroen Donkers. "THE ARIMAA MATCH." ICGA Journal 27, no. 2 (June 1, 2004): 109. http://dx.doi.org/10.3233/icg-2004-27208.

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Fotland, David. "Computer Arimaa: The Beginning." ICGA Journal 38, no. 1 (March 1, 2015): 12–18. http://dx.doi.org/10.3233/icg-2015-38103.

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Lewis, Andy. "Game Over, Arimaa?1." ICGA Journal 38, no. 1 (March 1, 2015): 55–62. http://dx.doi.org/10.3233/icg-2015-38108.

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Wu, David J. "Designing a Winning Arimaa Program." ICGA Journal 38, no. 1 (March 1, 2015): 19–40. http://dx.doi.org/10.3233/icg-2015-38104.

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Syed, Omar, and Jeroen Donkers. "The 2nd Arimaa Challenge Match." ICGA Journal 28, no. 2 (June 1, 2005): 118. http://dx.doi.org/10.3233/icg-2005-28212.

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Syed, Omar. "THE 5th ARIMAA CHALLENGE MATCH." ICGA Journal 31, no. 2 (June 1, 2008): 116. http://dx.doi.org/10.3233/icg-2008-31208.

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Syed, Omar. "The 7th Annual Arimaa Challenge Match." ICGA Journal 33, no. 2 (June 1, 2010): 108–9. http://dx.doi.org/10.3233/icg-2010-33206.

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Syed, Omar. "THE 5th ARIMAA COMPUTER WORLD CHAMPIONSHIP." ICGA Journal 31, no. 2 (June 1, 2008): 114–15. http://dx.doi.org/10.3233/icg-2008-31207.

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Syed, Omar. "The 6th Annual Arimaa Challenge Match." ICGA Journal 32, no. 2 (June 1, 2009): 119–20. http://dx.doi.org/10.3233/icg-2009-32221.

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Syed, Omar. "The Arimaa Challenge: From Inception to Completion." ICGA Journal 38, no. 1 (March 1, 2015): 3–11. http://dx.doi.org/10.3233/icg-2015-38102.

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Syed, Omar. "The 6th Annual Arimaa Computer World Championship." ICGA Journal 32, no. 1 (March 1, 2009): 56–57. http://dx.doi.org/10.3233/icg-2009-32114.

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12

Syed, Omar. "The 7th Annual Arimaa Computer World Championship." ICGA Journal 33, no. 1 (March 1, 2010): 45–46. http://dx.doi.org/10.3233/icg-2010-33107.

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Syed, Omar, and Aamir Syed. "ARIMAA - A NEW GAME DESIGNED TO BE DIFFICULT FOR COMPUTERS." ICGA Journal 26, no. 2 (June 1, 2003): 138–39. http://dx.doi.org/10.3233/icg-2003-26213.

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Kowalski, Jakub, Maksymilian Mika, Jakub Sutowicz, and Marek Szykuła. "Regular Boardgames." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1699–706. http://dx.doi.org/10.1609/aaai.v33i01.33011699.

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We propose a new General Game Playing (GGP) language called Regular Boardgames (RBG), which is based on the theory of regular languages. The objective of RBG is to join key properties as expressiveness, efficiency, and naturalness of the description in one GGP formalism, compensating certain drawbacks of the existing languages. This often makes RBG more suitable for various research and practical developments in GGP. While dedicated mostly for describing board games, RBG is universal for the class of all finite deterministic turn-based games with perfect information. We establish foundations of RBG, and analyze it theoretically and experimentally, focusing on the efficiency of reasoning. Regular Boardgames is the first GGP language that allows efficient encoding and playing games with complex rules and with large branching factor (e.g. amazons, arimaa, large chess variants, go, international checkers, paper soccer).

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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 (April 17, 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 forecasting models for 2 bedroom, 3 bedroom, 4 bedroom and 5 bedroom, than ARIMA models. Generally, both ARIMA and ARIMAX models are good for short term forecasting modelling.

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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 (December 30, 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 sampai dengan bulan Juni tahun 2019. Pemodelan ARIMAX dilakukan dengan menggabungkan model regresi dummy dari data aktual dan model ARIMA dari data residual.

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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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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 (July 31, 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 between ARIMAX and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). By taking such calendar variation into account, the result shows that ARIMAX-ANFIS is the best method in predicting non-cash transactions since it produces lower MAPE. It is indicated that non-cash transaction increases significantly ahead of Ied Fitr occurrence and hits the peak in December. It demonstrates that the hybrid model can improve the accuracy performance of prediction.

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Putera, Muhammad Luthfi Setiarno. "PERAMALAN TRANSAKSI PEMBAYARAN NON-TUNAI MENGGUNAKAN ARIMAX-ANN DENGAN KONFIGURASI KALENDER." BAREKENG: Jurnal Ilmu Matematika dan Terapan 14, no. 1 (March 1, 2020): 135–46. http://dx.doi.org/10.30598/barekengvol14iss1pp135-146.

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Akses internet yang luas mendorong kian seringnya sistem pembayaran non-tunai digunakan. Di Indonesia, berbagai aktivitas dan transaksi ekonomi seringkali dipengaruhi oleh pergerakan kalender, terutama kalender Hijriyah. Tujuan penelitian ini untuk memodelkan dan meramalkan total pembayaran non-tunai di Indonesia dengan menambahkan konfigurasi kalender sebagai variabel. Digunakan metode ARIMA, ARIMAX dan hibrida ARIMAX-ANN yang akan dibandingkan akurasinya. Diperoleh model terbaik untuk peramalan jumlah pembayaran non-tunai adalah ARIMAX-ANN dengan RMSE terkecil, yaitu Rp 20,9 triliun. Spesifikasi model terbaik tersebut adalah ARIMAX(2,1,1) yang dihibrida dengan ANN yang inputnya diseleksi melalui regresi stepwise. Selain memenuhi asumsi galat yang identik, independen, dan berdistribusi normal, ARIMAX-ANN juga mampu mengikuti dinamika dan tren dari pembayaran non-tunai, khususnya pada bulan jatuhnya Idul Fitri dan periode akhir tahun.

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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 según el modelo DCC (por sus siglas en inglés Dinamic Conditional Correlation) al par USD/COP genera pronósticos fuera de muestra con mejor desempeño que los modelos univariados ARIMA-GARCH.

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21

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

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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 the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)12. The ARIMAX model showed that there is a significant variation between the states in Sudan.

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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 (March 1, 2015): 7. http://dx.doi.org/10.11591/.v4i1.4705.

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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 the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)12. The ARIMAX model showed that there is a significant variation between the states in Sudan.

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Díaz Sosa, María Eliana, Edwin Andrés Cruz Pérez, and Wilmer Dario Pineda Ríos. "Modelamiento del precio de la papa criolla en el departamento de Cundinamarca por medio de series de tiempo y modelos dinámicos." Comunicaciones en Estadística 14, no. 1 (February 1, 2021): 31–52. http://dx.doi.org/10.15332/23393076.6633.

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El presente trabajo tiene como objetivo evaluar el comportamiento y pronóstico del precio de la papa criolla en el departamento de Cundinamarca, según los factores climáticos desde enero de 2012 hasta abril de 2018. Para ello, se tomaron en consideración, por un lado, análisis basados en series de tiempo (ARIMA, ARIMAX) y, por el otro, modelos lineales dinámicos (con y sin covariables). En los modelos trabajados se usaron como variables las condiciones climáticas de la zona en cuestión, a las cuales se les aplicó un método de imputación de datos debido a la ausencia de información. Luego fueron agrupados en tres factores construidos por Análisis Factorial para Series de Tiempo (TSFA). Finalmente, se procedió a comparar los indicadores de los cuatro modelos, llegando a la conclusión de que los modelos ARIMA Y ARIMAX generan las mejores predicciones respecto del precio de la papa criolla en el departamento de Cundinamarca.

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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 (January 12, 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. The results obtained showed that the data satisfied normality and stationarity requirements. The finding of the study showed that ARIMA model has least value of AIC of 1177.92, following the ARIMAX model with value of AIC=1542.25 and SEM recorded highest value of 1595.67. The findings further revealed that the ARIMA has the least values across the measures of accuracy. More so, five years predictions of the cases of diabetes mellitus were obtained using the models under study. From the results of the findings, ARIMA model proved to be best alternative for estimating reported cases of diabetes mellitus in Anambra state. Based on the findings, we recommend there is need for medical practitioners /health planners to create awareness and inform patients about the possible related risk factors of death through early diagnosis and intervention.

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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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YÜCESAN, MELİH. "YSA, ARIMA ve ARIMAX Yöntemleriyle Satış Tahmini: Beyaz Eşya Sektöründe bir Uygulama - Sales Forecast with YSA, ARIMA and ARIMAX Methods: An Application in the White Goods Sector." Journal of Business Research - Turk 10, no. 1 (March 30, 2018): 689–706. http://dx.doi.org/10.20491/isarder.2018.414.

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Assakhiy, Rasyada, Samsul Anwar, and A. R. Fitriana. "PERAMALAN REALISASI PENERIMAAN ZAKAT PADA BAITULMAL ACEH DENGAN MEMPERTIMBANGKAN EFEK DARI VARIASI KALENDER." Jurnal Ekonomi Pembangunan 27, no. 2 (December 31, 2019): 27–45. http://dx.doi.org/10.14203/jep.27.2.2019.27-45.

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Baitulmal Aceh merupakan sebuah lembaga pemerintah daerah Provinsi Aceh yang bertanggung jawab sebagai pengelola dan pendistribusi zakat, infak dan sedekah (ZIS). Peramalan potensi zakat pada masa yang akan datang dibutuhkan oleh Baitulmal Aceh sebagai salah satu landasan penyusunan kebijakan pengelolaan ZIS. Penelitian ini bertujuan untuk meramalkan potensi zakat yang terkumpul pada tahun 2018 dan 2019 dengan mempertimbangkan efek dari variasi kalender. Data yang digunakan dalam penelitian ini adalah data realisasi penerimaan zakat bulanan mulai dari bulan Januari 2015 hingga Desember 2017 yang diperoleh dari Baitulmal Aceh. Data tersebut dianalisis dengan model Autoregressive Integrated Moving Average with Exogenous Variable (ARIMAX) dan Seasonal Autoregressive Integrated Moving Average (SARIMA) sebagai model pembanding. Hasil penelitian menunjukkan bahwa model ARIMAX dengan orde ARIMA(2,0,2) (1,0,2)12, t, V1, ..., V11 jauh lebih baik daripada model SARIMA dengan orde ARIMA(0,1,2)(0,1,1)12 berdasarkan indikator ketepatan hasil ramalannya (RMSE dan MAPE). Realisasi penerimaan zakat pada tahun 2018 dan 2019 masing-masing diperkirakan sebesar Rp. 1.347.526.504 dan Rp. 1.359.728.268. Hasil peramalan tersebut dapat digunakan sebagai salah satu rujukan bagi Baitulmal Aceh dalam menyusun kebijakan pendistribusian zakat pada tahun-tahun yang akan datang.

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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 passengers at Adisucipto Airport, Yogyakarta using ARIMAX method to forecast the number of domestic airplane passengers and the effectiveness of domestic passengers at the international airport. The purpose of this research is to determine the best ARIMAX model and forecast airplane passengers in Adisucipto airport. The results will show the effectiveness of ARIMAX model with the effect of calendar variance on domestic airplane passenger forecasting at international airport. Based on the result of AIC and RMSE values, it shows that the ARIMAX(1,0,1) model with calendar variation is better than ARIMA(1,0,1) in predicting the number of airplane passengers at Yogyakarta Adisutjipto airport.

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Qadrini, Laila, Asrirawan Asrirawan, Nur Mahmudah, Muhammad Fahmuddin, and Ihsan Fathoni Amri. "Forecasting Bank Indonesia Currency Inflow and Outflow Using ARIMA, Time Series Regression (TSR), ARIMAX, and NN Approaches in Lampung." Jurnal Matematika, Statistika dan Komputasi 17, no. 2 (December 23, 2020): 166–77. http://dx.doi.org/10.20956/jmsk.v17i2.11803.

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There are various types of data, one of which is the time-series data. This data type is capable of predicting future data with a similar speed as the forecasting method of analysis. This method is applied by Bank Indonesia (BI) in determining currency inflows and outflows in society. Moreover, Inflows and outflows of currency are monthly time-series data which are assumed to be influenced by time. In this study, several forecasting methods were used to predict this flow of currency including ARIMA, Time Series Regression (TSR), ARIMAX, and NN. Furthermore, RMSE accuracy was used in selecting the best method for predicting the currency flow. The results showed that the ARIMAX method was the best for forecasting because this method had the smallest RMSE.

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Titi, Dea Astri, Heri Kuswanto, and Suhartono Suhartono. "PERAMALAN LANGSUNG DAN TIDAK LANGSUNG MARKET SHARE MOBIL MENGGUNAKAN ARIMAX DENGAN EFEK VARIASI KALENDER." MEDIA STATISTIKA 13, no. 1 (June 19, 2020): 47–59. http://dx.doi.org/10.14710/medstat.13.1.47-59.

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Based on BPS data, the transportation industry sector contributed to about 8.01% of Indonesia's economic growth. The rapid growth of the transportation industry is also followed by the development of the automotive industry in Indonesia. The Exclusive Lisencee Agent of the Astra International group won a market share of 57% in April 2017. PT. Astra Daihatsu Motor, which is one of its subsidiaries, has a very rapid sales increase of 15% every year until Daihatsu's market share rises to 17.3%. Data from the Gabungan Industri Kendaraan Bermotor Indonesia (Gaikindo) shows an upward trend in car sales a month before Idul Fitri. This study carried out Daihatsu's direct and indirect market share forecasting using ARIMAX with a variety of calendar effects consisting of trends, monthly seasonal effects and Idul Fitri effects. The results indicated that indirect forecasting through forecasting the car sales for each brand and total market using ARIMAX outperforms the others and is able to capture the pattern of the testing data. The resulting SMAPE value of ARIMAX is smaller than direct forecasting and indirect forecasting using ARIMA.

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Rahmi, Nur Silviyah. "PERAMALAN INFLOW UANG KARTAL BANK INDONESIA KPW TASIKMALAYA JAWA BARAT DENGAN METODE KLASIK DAN MODERN." Jurnal Statistika Universitas Muhammadiyah Semarang 8, no. 2 (November 30, 2020): 166. http://dx.doi.org/10.26714/jsunimus.8.2.2020.166-174.

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Ketersediaan uang kartal di Bank Indonesia (BI) dapat ditinjau melalui arus keluar masuknya uang kartal yang disebut dengan istilah inflow. Banyaknya uang yang beredar di masyarakat akan berpengaruh pada kondisi perekonomian suatu negara, sehingga Bank Indonesia (BI) menyusun perencanaan kebutuhan uang rupiah. Penelitian ini bertujuan untuk meramalkan inflow uang kartal di KPw Bank Indonesia (BI) Tasikmalaya dengan menggunakan pemodelan ARIMA, ARIMAX, Metode Dekomposisi, Metode Winter’s, MLP (Multilayer Perceptron) atau FFNN (Feed Forward Neural Network), Regresi Time Series, Metode Naïve dan Model Hybrid. Dari delapan metode runtun waktu tersebut baik klasik maupun modern akan dicari metode mana yang memberikan hasil akurasi ramalan yang terbaik dengan kriteria RMSE, MAPE dan MAD. Kesimpulan yang dihasilkan yaitu Hybrid ARIMA-NN yang merupakan gabungan dari model ARIMA dengan neural network tidak menjamin kinerja hasil peramalan yang lebih baik. Seperti yang disebutkan dalam hasil M3 Competition, semakin kompleks metode yang digunakan belum tentu metode tersebut menghasilkan akurasi yang lebih baik dibandingkan metode sederhana (klasik). Pada ramalan data inflow KPw BI Tasikmalaya Jawa Barat ini, menghasilkan kesimpulan bahwa metode regresi time series memiliki nilai kriteria pemodelan paling kecil dibandingkan dengan metode lainnya.

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Munir, Said, and Martin Mayfield. "Application of Density Plots and Time Series Modelling to the Analysis of Nitrogen Dioxides Measured by Low-Cost and Reference Sensors in Urban Areas." Nitrogen 2, no. 2 (April 13, 2021): 167–95. http://dx.doi.org/10.3390/nitrogen2020012.

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Temporal variability of NO2 concentrations measured by 28 Envirowatch E-MOTEs, 13 AQMesh pods, and eight reference sensors (five run by Sheffield City Council and three run by the Department for Environment, Food and Rural Affairs (DEFRA)) was analysed at different time scales (e.g., annual, weekly and diurnal cycles). Density plots and time variation plots were used to compare the distributions and temporal variability of NO2 concentrations. Long-term trends, both adjusted and non-adjusted, showed significant reductions in NO2 concentrations. At the Tinsley site, the non-adjusted trend was −0.94 (−1.12, −0.78) µgm−3/year, whereas the adjusted trend was −0.95 (−1.04, −0.86) µgm−3/year. At Devonshire Green, the non-adjusted trend was −1.21 (−1.91, −0.41) µgm−3/year and the adjusted trend was −1.26 (−1.57, −0.83) µgm−3/year. Furthermore, NO2 concentrations were analysed employing univariate linear and nonlinear time series models and their performance was compared with a more advanced time series model using two exogenous variables (NO and O3). For this purpose, time series data of NO, O3 and NO2 were obtained from a reference site in Sheffield, which were more accurate than the measurements from low-cost sensors and, therefore, more suitable for training and testing the model. In this article, the three main steps used for model development are discussed: (i) model specification for choosing appropriate values for p, d and q, (ii) model fitting (parameters estimation), and (iii) model diagnostic (testing the goodness of fit). The linear auto-regressive integrated moving average (ARIMA) performed better than the nonlinear counterpart; however, its performance in predicting NO2 concentration was inferior to ARIMA with exogenous variables (ARIMAX). Using cross-validation ARIMAX demonstrated strong association with the measured concentrations, with a correlation coefficient of 0.84 and RMSE of 9.90. ARIMAX can be used as an early warning tool for predicting potential pollution episodes in order to be proactive in adopting precautionary measures.

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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 (July 6, 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, Expenditure, FTSE AS Index, Gross Domestic Product (GDP), and Inflation are all explanatory variables selected for the research. Findings – The modelling results confirm that increased model complexity does not necessarily yield greater forecasting accuracy. The analysis shows that although the more complex VAR specification is amongst the best fitting models, its accuracy in producing out-of-sample forecasts is poorer than of some less complex specifications. The average Theil’s U-value for VAR model is around 0.65, which is higher than that of less complex SR with Expenditure (0.176) or ARIMAX (3,0,3) with GDP (0.31) as an explanatory variable models. Practical implications – The paper calls analysts to make forecasts more user-friendly, which are easy to use or understand, and for researchers to pay greater attention to the development and improvement of simpler forecasting techniques or simplification of more complex structures. Originality/value – The paper addresses the issue of complexity in modelling commercial property market. It advocates for simplicity in modelling and forecasting.

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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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Narsimhaiah, Lakshmi, P. K. Sahu, Kanchan Sinha, Sh Herojit Singh, Soumik Dey, and Pramit Pandit. "Forecasting of Coconut Production in India: An approach with ARIMA, ARIMAx and Combined Forecast Techniques." International Journal of Current Microbiology and Applied Sciences 8, no. 11 (November 10, 2019): 1710–19. http://dx.doi.org/10.20546/ijcmas.2019.811.199.

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Anggraeni, Wiwik, Retno Aulia Vinarti, and Yuni Dwi Kurniawati. "Performance Comparisons between Arima and Arimax Method in Moslem Kids Clothes Demand Forecasting: Case Study." Procedia Computer Science 72 (2015): 630–37. http://dx.doi.org/10.1016/j.procs.2015.12.172.

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Zhang, Rui, Zhen Guo, Yujie Meng, Songwang Wang, Shaoqiong Li, Ran Niu, Yu Wang, Qing Guo, and Yonghong Li. "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 (June 7, 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 the daily incidence of HFMD by using the data of the training set. The forecasting performances of the four fitted models were verified by using the data of the test set. Root mean square error (RMSE) was selected as the main measure to evaluate the performance of the models. Results: The RMSE for multivariate LSTM, univariate LSTM, ARIMA and ARIMAX (Autoregressive Integrated Moving Average Model with Exogenous Input Variables) was 10.78, 11.20, 12.43 and 14.73, respectively. The LSTM model with exogenous meteorological variables has the best performance among the four models and meteorological variables can increase the prediction accuracy of LSTM model. For the ARIMA model, exogenous meteorological variables did not increase the prediction accuracy but became the interference factor of the model. Conclusions: Multivariate LSTM is the best among the four models to fit the daily incidence of HFMD in Ningbo. It can provide a scientific method to build the HFMD early warning system and the methodology can also be applied to other communicable diseases.

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Barzola-Monteses, Julio, Mónica Mite-León, Mayken Espinoza-Andaluz, Juan Gómez-Romero, and Waldo Fajardo. "Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case." Sustainability 11, no. 23 (November 20, 2019): 6539. http://dx.doi.org/10.3390/su11236539.

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Electrical generation in Ecuador mainly comes from hydroelectric and thermo-fossil sources, with the former amounting to almost half of the national production. Even though hydroelectric power sources are highly stable, there is a threat of droughts and floods affecting Ecuadorian water reservoirs and producing electrical faults, as highlighted by the 2009 Ecuador electricity crisis. Therefore, predicting the behavior of the hydroelectric system is crucial to develop appropriate planning strategies and a good starting point for energy policy decisions. In this paper, we developed a time series predictive model of hydroelectric power production in Ecuador. To this aim, we used production and precipitation data from 2000 to 2015 and compared the Box-Jenkins (ARIMA) and the Box-Tiao (ARIMAX) regression methods. The results showed that the best model is the ARIMAX (1,1,1) (1,0,0)12, which considers an exogenous variable precipitation in the Napo River basin and can accurately predict monthly production values up to a year in advance. This model can provide valuable insights to Ecuadorian energy managers and policymakers.

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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 Networks have poor prognostic ability (as in the case considered by us, when modeling insufficiently long series of dynamics). Therefore, when modeling and forecasting the dynamics of the Ukraine-EU trade balance, classical (so-called Box-Jenkins) time series models were used. In this case, the time series is divided into several components (in our case, terms): the main trend is the trend, the seasonal component and the random component (the so-called white noise). By smoothing the initial series, a trend was found, and an analysis of the autocorrelation functions revealed a one-year seasonality. Eliminating the trend and the seasonal component, we obtained a random component, which has a Gaussian distribution. This made it possible to apply first the ARIMA* ARIMAS additive model, and then the Holt model of exponential smoothing with a damped trend. Adequate models of Ukraine-EU trade balance dynamics have been obtained, according to which the forecast has been made. A comparative analysis of the models used. The best model was chosen for forecasting, which allowed to get a good forecast (in comparison with actual data).

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Chadsuthi, Sudarat, Sopon Iamsirithaworn, Wannapong Triampo, and Charin Modchang. "Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses." Computational and Mathematical Methods in Medicine 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/436495.

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Influenza is a worldwide respiratory infectious disease that easily spreads from one person to another. Previous research has found that the influenza transmission process is often associated with climate variables. In this study, we used autocorrelation and partial autocorrelation plots to determine the appropriate autoregressive integrated moving average (ARIMA) model for influenza transmission in the central and southern regions of Thailand. The relationships between reported influenza cases and the climate data, such as the amount of rainfall, average temperature, average maximum relative humidity, average minimum relative humidity, and average relative humidity, were evaluated using cross-correlation function. Based on the available data of suspected influenza cases and climate variables, the most appropriate ARIMA(X) model for each region was obtained. We found that the average temperature correlated with influenza cases in both central and southern regions, but average minimum relative humidity played an important role only in the southern region. The ARIMAX model that includes the average temperature with a 4-month lag and the minimum relative humidity with a 2-month lag is the appropriate model for the central region, whereas including the minimum relative humidity with a 4-month lag results in the best model for the southern region.

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Ray, Soumik, and Banjul Bhattacharyya. "STATISTICAL MODELING AND FORECASTING OF ARIMA AND ARIMAX MODELS FOR FOOD GRAINS PRODUCTION AND NET AVAILABILITY OF INDIA." Journal of Experimental Biology and Agricultural Sciences 8, no. 3 (June 25, 2020): 296–309. http://dx.doi.org/10.18006/2020.8(3).296.309.

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Chemere, Befekadu, Jiyung Kim, Baehun Lee, Moonju Kim, Byongwan Kim, and Kyungil Sung. "Detecting Long-Term Dry Matter Yield Trend of Sorghum-Sudangrass Hybrid and Climatic Factors Using Time Series Analysis in the Republic of Korea." Agriculture 8, no. 12 (December 11, 2018): 197. http://dx.doi.org/10.3390/agriculture8120197.

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Despite the gradual increase in livestock feed demands, the supply faces enormous challenges due to extreme climatic conditions. As the presence of these climatic condition has the potential to affect the yield of sorghum-sudangrass hybrid (SSH), understanding the yield variation in relation to the climatic conditions provides the ability to come up with proper mitigation strategies. This study was designed to detect the effect of climatic factors on the long-term dry matter yield (DMY) trend of SSH using time series analysis in the Republic of Korea. The collected data consisted of DMY, seeding-harvesting dates, the location where the cultivation took place, cultivars, and climatic factors related to cultivation of SSH. Based on the assumption of normality, the final data set (n = 420) was generated after outliers had been removed using Box-plot analysis. To evaluate the seasonality of DMY, an augmented Dickey Fuller (ADF) test and a correlogram of Autocorrelation Function (ACF) were used. Prior to detecting the effect of climatic factors on the DMY trend, the Autoregressive Integrated Moving Average (ARIMA) model was fitted to non-seasonal DMY series, and ARIMA (2, 1, 1) was found to be the optimal model to describe the long-term DMY trend of SSH. ARIMA with climatic factors (ARIMAX) detected significance (p < 0.05) of Seeding-Harvesting Precipitation Amount (SHPA) and Seeding-Harvesting Accumulated Temperature (SHAMT) on DMY trend. This does not mean that the average temperature and duration of exposure to sunshine do not affect the growth and development of SSH. The result underlines the impact of the precipitation model as a major factor for the seasonality of long-term DMY of SSH in the Republic of Korea.

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Grillenzoni, Carlo. "ARIMA Processes with ARIMA Parameters." Journal of Business & Economic Statistics 11, no. 2 (April 1993): 235. http://dx.doi.org/10.2307/1391375.

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Grillenzoni, Carlo. "ARIMA Processes With ARIMA Parameters." Journal of Business & Economic Statistics 11, no. 2 (April 1993): 235–50. http://dx.doi.org/10.1080/07350015.1993.10509952.

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Du, Zhicheng, Lin Xu, Wangjian Zhang, Dingmei Zhang, Shicheng Yu, and Yuantao Hao. "Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China." BMJ Open 7, no. 10 (October 2017): e016263. http://dx.doi.org/10.1136/bmjopen-2017-016263.

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ObjectivesHand, foot, and mouth disease (HFMD) has caused a substantial burden in China, especially in Guangdong Province. Based on the enhanced surveillance system, we aimed to explore whether the addition of temperate and search engine query data improves the risk prediction of HFMD.DesignEcological study.Setting and participantsInformation on the confirmed cases of HFMD, climate parameters and search engine query logs was collected. A total of 1.36 million HFMD cases were identified from the surveillance system during 2011–2014. Analyses were conducted at aggregate level and no confidential information was involved.Outcome measuresA seasonal autoregressive integrated moving average (ARIMA) model with external variables (ARIMAX) was used to predict the HFMD incidence from 2011 to 2014, taking into account temperature and search engine query data (Baidu Index, BDI). Statistics of goodness-of-fit and precision of prediction were used to compare models (1) based on surveillance data only, and with the addition of (2) temperature, (3) BDI, and (4) both temperature and BDI.ResultsA high correlation between HFMD incidence and BDI (r=0.794, p<0.001) or temperature (r=0.657, p<0.001) was observed using both time series plot and correlation matrix. A linear effect of BDI (without lag) and non-linear effect of temperature (1 week lag) on HFMD incidence were found in a distributed lag non-linear model. Compared with the model based on surveillance data only, the ARIMAX model including BDI reached the best goodness-of-fit with an Akaike information criterion (AIC) value of −345.332, whereas the model including both BDI and temperature had the most accurate prediction in terms of the mean absolute percentage error (MAPE) of 101.745%.ConclusionsAn ARIMAX model incorporating search engine query data significantly improved the prediction of HFMD. Further studies are warranted to examine whether including search engine query data also improves the prediction of other infectious diseases in other settings.

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Shinkarenko, Volodymyr, Alexey Hostryk, Larysa Shynkarenko, and Leonid Dolinskyi. "A forecasting the consumer price index using time series model." SHS Web of Conferences 107 (2021): 10002. http://dx.doi.org/10.1051/shsconf/202110710002.

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This article examines the behavior of the consumer price index in Ukraine for the period from January 2010 to September 2020. The characteristics of the initial time series, the analysis of autocorrelation functions made it possible to reveal the tendency of their development and the presence of annual seasonality. To model the behavior of the consumer price index and forecast for the next months, two types of models were used: the additive ARIMA*ARIMAS model, better known as the model of Box-Jenkins and the exponential smoothing model with the seasonality estimate of Holt-Winters. As a result of using the STATISTICA package, the most adequate models were built, reflecting the monthly dynamics of the consumer price index in Ukraine. The inflation forecast was carried out on the basis of the Holt-Winters model, which has a minimum error.

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Xiao, Wen, Ning Lou, Hailong Ruan, Lin Bao, Zhiyong Xiong, Changfei Yuan, Junwei Tong, et al. "Mir-144-3p Promotes Cell Proliferation, Metastasis, Sunitinib Resistance in Clear Cell Renal Cell Carcinoma by Downregulating ARID1A." Cellular Physiology and Biochemistry 43, no. 6 (2017): 2420–33. http://dx.doi.org/10.1159/000484395.

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Background/Aims: We previously performed microRNA (miRNA) microarray to identify effective indicators of clear cell renal cell carcinoma (ccRCC) tissue samples and preoperative/postoperative plasma in which we identified miR-144-3p as an oncomiRNA. However, the molecular mechanism of miR-144-3p remains unclear. This study aims to explore the roles of miR-144-3p in the invasion, migration and Sunitinib-resistance in ccRCC and to elucidate the underlying mechanisms. Methods: Gain and loss of function approaches were used to investigate the cell proliferation, cycle distribution, clonogenicity, migration, invasion, chemosensitivity of miR-144-3p in vitro. The xenograft model was used to assess the effects of miR-144-3p overexpression on tumorigenesis. Bioinformatics analysis and dual-luciferase reporter assay were used to indentify AT-rich interactive domain 1A (ARID1A) as a direct target gene of miR-144-3p. Quantitative RT-PCR, Western blotting, and immunohistochemical (IHC) staining were used to explore ARID1A expression level of the mRNA and protein. Results: We found that miR-144-3p overexpression enhanced cell proliferation, clonogenicity, migration, invasion, and chemoresistance in ccRCC cells. Notably, the oncotumor activities of miR-144-3p were mediated by repressing the expression of ARID1A. The downregulation of ARIDIA could promote the function of miR-144-3p in cell proliferation, metastasis and chemoresistance. Consistently, ARID1A mRNA and protein levels were decreased in ccRCC and in nude mice, and they negatively correlated with miR-144-3p. Conclusion: Higher miR-144-3p may enhance malignancy and resistance to Sunitinib in ccRCC by targeting ARID1A, the observations may uncover novel strategies of ccRCC treatment.

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Kumar, Manish, and M. Thenmozhi. "Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models." International Journal of Banking, Accounting and Finance 5, no. 3 (2014): 284. http://dx.doi.org/10.1504/ijbaaf.2014.064307.

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Respondek, G., J. Gröger, J. Floeter, and A. Temming. "Variability of fishing effort for the German brown shrimp (Crangon crangon) fishing fleet: influencing factors, and seasonal and spatial patterns." ICES Journal of Marine Science 71, no. 7 (February 26, 2014): 1805–17. http://dx.doi.org/10.1093/icesjms/fsu016.

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Abstract We examined factors affecting the fishing effort in the German brown shrimp (Crangon crangon) fishery, including shrimp and fuel price, catch per unit effort (cpue), number of days with strong wind, fishing port, and season. Time-series analysis (TS) using rational transfer functions (ARIMAX, an extension of autoregressive integrated moving average (ARIMA) modelling) were applied to model mean monthly boat effort (MBE, in hours), aggregated from the logbook data of five German ports for the years 2000–2008. We addressed two major areas: (i) whether cyclic effort patterns and the effect of external factors such as wind differ regionally, and (ii) whether external factors such as cpue, shrimp and fuel prices have a significant influence on the effort pattern. The estimated ARIMAX models accounted for 52–77% of the variance in MBE if analysed separately for each port, indicating a stable seasonal pattern of fishing effort without major interannual variations or significant trends. In spite of considerable variability in the external factors, none of cpue, shrimp or fuel price had a significant effect on the fishing effort. We interpreted this stability as a lack of alternative target species and discuss the theory that a response of a fishery to these external factors might only occur once certain critical threshold values have been exceeded, which was not the case during the investigated period. The models indicated differences in the seasonal patterns of the different harbours, with shorter trips and an earlier start for the main fishing season in the southwest (SW) regions, whereas in the northeast (NE) harbours, trips were longer, peak effort was shifted by 1–2 months and the spring season was of greater importance. The relationships of these regional patterns to the shrimp life cycle and coast topography, as well as implications for future management, are discussed.

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Ahmar, Ansari Saleh, Suryo Guritno, Abdurakhman, Abdul Rahman, Awi, Alimuddin, Ilham Minggi, et al. "Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)." Journal of Physics: Conference Series 954 (January 2018): 012010. http://dx.doi.org/10.1088/1742-6596/954/1/012010.

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Journal articles on the topic 'Arimaa'

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