Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory

Matyjaszek, Marta; Fernández, Pedro Riesgo; Krzemień, Alicja; Wodarski, Krzysztof; Valverde, Gregorio Fidalgo

doi:10.1016/j.resourpol.2019.02.017

Cited by 77 publications

(36 citation statements)

References 32 publications

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“…Given that the time series forecasting can lose its prominence in the long run and sometimes is affected by new and frequent variables [12], the forecast horizon of this study was a medium-term horizon until 2022. The main application of the time series analysis is forecasting.…”

Section: Methodsmentioning

confidence: 99%

Application of a Statistical Model to Forecast Drowning Deaths in Iran

Omidi

Eskandari

Raissi

et al. 2019

HDQ

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Section: Methodsmentioning

confidence: 99%

Application of a Statistical Model to Forecast Drowning Deaths in Iran

Omidi

Eskandari

Raissi

et al. 2019

HDQ

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“…ARIMA have relished beneficial applications in forecasting insurance, economics, energy, engineering, social, stock problems, and foreign exchange [72][73][74][75]. Critical literature review show that the accuracy of SC and ML techniques is superior to the ARIMA models in predicting time series problems [76][77][78][79][80].…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

Moghaddam

Monjezi

et al. 2020

Applied Sciences

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Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques i.e., autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural network (ANN), and classification and regression tree (CART) were developed for prediction of monthly iron ore price. Then, using testing datasets, the developed models were validated and their performance capacities were compared. The results showed that performance prediction of the GMDH model is significantly better than other predictive models based on four performance indices i.e., root mean square error, variance account for (VAF), mean absolute error, and mean absolute percentage error. Results of VAF (97.89%, 90.81%, 80.95%, 55.02%, and 23.87% for GMDH, SVR, ANN, CART, and ARIMA models, respectively) revealed that the GMDH technique is able to predict iron ore price with higher degree of accuracy compared to the other techniques.

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“…Peramalan nilai inflasi padi-padian, umbi-umbian, dan hasilnya tertera pada Tabel 3.Subkelompok padi-padian, umbiumbian, dan hasil-hasilnya terdiri atas komoditas beras, ketela pohon, mie basah, mie kering instant, tepung beras, dan tepung terigu.Berdasarkan perbandingan nilai inflasi padi-padian, umbi-umbian, dan hasil-hasilnya antara hasil peramalan dan nilai aktual, terlihat bahwa nilai peramalan pada tahun 2019 rata-rata lebih tinggi dari nilai inflasi aktual karena metode yang digunakan dalam peramalan ini adalah metode statik yang meramalkan nilai inflasi satu langkah kedepan sampai dengan waktu sekarang (Matyjaszek, Riesgo Fernández, Krzemień, Wodarski, & Fidalgo Valverde, 2019;Torbat, Khashei, & Bijari, 2018).Dibandingkan dengan nilai peramalan inflasi pada tahun 2020 ratarata nilai inflasi lebih rendah, mengingat perkiraan bulan Ramadhan dan Idul Fitri tahun 2020 adalah bulan April dan Mei, kemungkinan nilai inflasi aktual pada saat itu bisa lebih tinggi atau lebih rendah tergantung kebijakan pemerintah daerah provinsi Sulawesi Tengah dan Kota Palu dalam mengantisipasi situasi tersebut.…”

Section: Padi-padian Umbi-umbian Dan Hasilhasilnyaunclassified

Model Peramalan Inflasi Bahan Makanan Primer dengan Pendekatan Box-Jenkins: Studi kasus di Kota Palu

Rauf

Lamusa

Bahri

et al. 2020

Agriekonomika Jurnal Sosial EKonomi dan Pertanian

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Inflasi merupakan naiknya harga barang dan jasa secara umum dan berkelanjutan pada periode tertentu. Inflasi pada umumnya disebabkan oleh peningkatan permintaan agregat, kenaikan biaya produksi serta perkiraan nilai inflasi pada masa yang akan datang. Inflasi merupakan data deret waktu yang sulit diprediksi karena mengandung komponen tren, musiman, siklus, dan acak. Penelitian ini bertujuan untuk mencari model terbaik inflasi bahan makanan primer, menggunakan model ARIMA. Model terbaik yang diperoleh berdasarkan subkelompok bahan makanan adalah padi-padian, umbi-umbian,

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Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory

Cited by 77 publications

References 32 publications

Application of a Statistical Model to Forecast Drowning Deaths in Iran

Application of a Statistical Model to Forecast Drowning Deaths in Iran

Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

Model Peramalan Inflasi Bahan Makanan Primer dengan Pendekatan Box-Jenkins: Studi kasus di Kota Palu

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