ARIMA forecasting of China’s coal consumption, price and investment by 2030

Jiang, Shumin; Yang, Chen; Guo, Jingtao; Ding, Zhanwen

doi:10.1080/15567249.2017.1423413

Cited by 63 publications

(39 citation statements)

References 8 publications

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“…The application of wavelet transform further reduces the error of predictions performed by the model. In a similar way, other works related to the forecast of energy have been discussed in [9], where the authors described an ARIMA model for forecast coal's consumption in China, and in [10] where authors used it to forecast energy consumption of Turkey. One more example can be found in [11] where authors describe the use of exogenous variables for residential low voltage distribution networks.…”

Section: Related Workmentioning

confidence: 98%

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Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data

et al. 2018

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E-commerce is becoming more and more the main instrument for selling goods to the mass market. This led to a growing interest in algorithms and techniques able to predict products future prices, since they allow us to define smart systems able to improve the quality of life by suggesting more affordable goods and services. The joint use of time series, reputation and sentiment analysis clearly represents one important approach to this research issue. In this paper we present Price Probe, a suite of software tools developed to perform forecasting on products’ prices. Its primary aim is to predict the future price trend of products generating a customized forecast through the exploitation of autoregressive integrated moving average (ARIMA) model. We experimented the effectiveness of the proposed approach on one of the biggest E-commerce infrastructure in the world: Amazon. We used specific APIs and dedicated crawlers to extract and collect information about products and their related prices over time and, moreover, we extracted information from social media and Google Trends that we used as exogenous features for the ARIMA model. We fine-estimated ARIMA’s parameters and tried the different combinations of the exogenous features and noticed through experimental analysis that the presence of Google Trends information significantly improved the predictions.

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Section: Related Workmentioning

confidence: 98%

“…Similar studies using ARIMA for price forecasting have been done in [4][5][6][7][8][9][10][11]. Other works focused on the forecasting of e-commerce prices have been proposed in [12] where authors proposed LSTM neural networks to obtain better prediction performances in daily phone prices in one particular marketplace, amazon.fr.…”

Section: Introductionmentioning

confidence: 98%

Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data

et al. 2018

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“…If k < iter max , continue to iterate back to step 3, else print optimum solution. (Zhou, 2018), autoregressive integrated moving average model (ARIMA) (Jiang et al, 2018). In the PR(n) model, "n" is the number of polynomial regressions.…”

Section: Optimization Step Of Parametersmentioning

confidence: 99%

Application of a new information priority accumulated grey model with time power to predict short-term wind turbine capacity

Xia

Huang

et al. 2020

Journal of Cleaner Production

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Wind energy makes a significant contribution to global power generation. Predicting wind turbine capacity is becoming increasingly crucial for cleaner production. For this purpose, a new information priority accumulated grey model with time power is proposed to predict short-term wind turbine capacity. Firstly, the computational formulas for the time response sequence and the prediction values are deduced by grey modeling technique and the definite integral trapezoidal approximation formula. Secondly, an intelligent algorithm based on particle swarm optimization is applied to determine the optimal nonlinear parameters of the novel model. Thirdly, three real numerical examples are given to examine the accuracy of the new model by comparing with six existing prediction models. Finally, based on the wind turbine capacity from 2007 to 2017, the proposed model is established to predict the total wind turbine capacity in Europe, North America, Asia, and the world. The numerical results reveal that the novel model is superior to other forecasting models. It has a great advantage for small samples with new characteristic behaviors.Besides, reasonable suggestions are put forward from the standpoint of the practitioners and governments, which has high potential to advance the sustainable improvement of clean energy production in the future. . Application of a new information priority accumulated grey model with time power to predict short-term wind turbine capacity.

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“…This technique is one of the most routinely used statistical approaches for time series predicting over the past decades. 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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ARIMA forecasting of China’s coal consumption, price and investment by 2030

Cited by 63 publications

References 8 publications

Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data

Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data

Application of a new information priority accumulated grey model with time power to predict short-term wind turbine capacity

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

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