Technical Forecasting Research Articles

Forecasts of thermal coal prices through Gaussian process regressions

Given thermal coal's significance as a tactical energy source, price projections for the commodity are crucial for investors and decision-makers alike. The goal of the current work is to determine whether Gaussian process regressions are useful for this forecast problem using a dataset of closing prices of thermal coal traded on the China Zhengzhou Commodity Exchange from January 4, 2016, to December 31, 2020. This is a significant financial index that has not received enough attention in the literature in terms of price forecasting. Our forecasting exercises make use of Bayesian optimizations and cross-validation. The price from January 02, 2020, to December 31, 2020 is successfully predicted by the generated models, with the out-of-sample relative root mean square error of 0.4210%. Gaussian process regressions are shown to be useful for the thermal coal price forecast problem. The outcomes of this projection might be used as independent technical forecasts or in conjunction with other forecasts for policy research that entails developing viewpoints on price patterns.

Carbon emission allowance price forecasting for China Guangdong carbon emission exchange via the neural network

Carbon emission allowance price forecasting is a significant issue for policy makers and investors with the world transitioning to green energy and devoting enormous efforts to be more sustainable. This study explores usefulness of the nonlinear autoregressive neural network for this forecasting problem in a dataset of daily closing prices of carbon emission allowances traded in China Guangdong Carbon Emission Exchange during 19 December 2013–20 August 2021. Through examining various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, the model leading to generally accurate and stable performance is reached. Usefulness of the machine learning technique for the price forecasting problem of the carbon emission allowance price is illustrated. Results here might be used on a standalone basis as technical forecasts or combined with fundamental forecasts to form perspectives of price trends and perform policy analysis, which could better assist different stakeholders in understanding energy cost and planning for green transition.

Platinum and palladium price forecasting through neural networks

To many commodity market participants, forecasts of price series represent a critical task. In this work, nonlinear autoregressive neural network models’ potential is explored for forecasting daily prices series of platinum and palladium over about a fifty-year period. For this purpose, one hundred and twenty model settings are examined, including different training algorithms, numbers of hidden neurons and delays, and ratios used to segment the data. With the analysis, two models leading to stable and accurate forecast results are constructed for the prices of the two commodities. In particular, the models’ performance in terms of the relative root mean square error is 1.86% and 3.61% for platinum and palladium, respectively, for the overall sample. Results in this work could help technical forecasts and policy analysis. The forecast framework might be extended to other different commodities.

Технологическое лидерство государства: концептуальное понимание и механизмы формирования

The article shows that the technological leadership of the state should conceptually be based on such institutions as technological sovereignty, technological equality and technological cooperation. It is noted that the technological leadership of the state is a multidimensional category, including such elements as technology, scientific, technological and educational base, platforms, and interactions. The differences between the technological leadership of the company and the state are outlined. Using the author's approach to the concepts of "technological sovereignty" and "technological cooperation and equality", the mechanisms of technological leadership of the state are highlighted, which can be used by both the initiator states of partnerships and participants in these partnerships. These include, in particular, leading companies, government policies that support the effective allocation of national resources, the external contour of national leadership development, the system of scientific and technical forecasting and analytics, the national system of "soft power", joint ownership of intellectual property by friendly states, complete technological chains, platforms and standards formed within the framework of partnerships, and etc. Using the example of the state corporation Rosatom, the possibility of using leading companies as a mechanism for technological leadership in Russia is considered.

National Center of Physics and Mathematics – a new form of organization of science

The article deals with the problem of activating fundamental scientific activity in the Russian Federation by combining the efforts of various scientific institutes – institutions of the Academy of Sciences, universities, industry and corporate science within the framework of the National Center for Physics and Mathematics (NCPM). The purpose of the article is to identify institutional features and substantiate promising organizational tasks of the NCFM, the main research methods are institutional, normative, comparative analysis. Institutionally, the NCPM will make it possible to get away from the existing division of science along departmental lines and related problems. It is proved that the use of the organizational “CERN model” in the NCPM will be able to attract a wide range of scientists from various Russian and, in the future, foreign organizations, will help the formation of a collaboration for conducting experiments and research at scientific installations of the “megasience” class. This will make it possible to form and work out a national model of the mobility of scientists. The territorial and personnel aspects of the NCFM are considered, measures are proposed to expand the types of activities, to activate the influx of students and postgraduates for subsequent work in scientific and knowledge-intensive industrial organizations, to reflect the peculiarities of the development of the territory of the NCFM location in strategic documents of various levels. Conclusions are drawn about the positive impact of the activities of the NCPM on socio-economic processes; about new opportunities for diversification of the military-industrial complex associated with the development of fundamental science; about accelerating the formation of a system of technological transfer; contributing to the organization of the system of strategic planning and scientific and technical forecasting in the country

Composite property price index forecasting with neural networks

PurposeThe Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.Design/methodology/approachThe goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.FindingsThe authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.Originality/valueResults here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

Japan 2040: Dialectics of Transhumanism and Society of the Future

Analysis of the essence, content and forms of the scenario state of Japan in 2040, reflected in the 11th Scientific and Technical Forecast of NISTEP in 2019, revealed a number of conceptual dialectical contradictions. They narrow down to the question of admissibility and expediency of changing a man’s nature in order to ensure his prosperous, safe, meaningful and happy existence. The author proposes for discussion a conclusion on inevitability of the transhumanization of a mankind on the scale of a single country (Japan) and the whole world, given the nature of the great challenges facing it. The possibility of keeping the historical development in a conditionally humanistic direction is noted, given the emphasis of social reforms in Japan, reflected in the 6th Basic Plan for scientific, technical and innovative development of the country, on building a society for the fullest realization and use of human intellectual potential.

Japan 2040: Dialectics of Transhumanism and Society of the Future

Analysis of the essence, content and forms of the scenario state of Japan in 2040, reflected in the 11th Scientific and Technical Forecast of NISTEP in 2019, revealed a number of conceptual dialectical contradictions. They narrow down to the question of admissibility and expediency of changing a man’s nature in order to ensure his prosperous, safe, meaningful and happy existence. The author proposes for discussion a conclusion on inevitability of the transhumanization of a mankind on the scale of a single country (Japan) and the whole world, given the nature of the great challenges facing it. The possibility of keeping the historical development in a conditionally humanistic direction is noted, given the emphasis of social reforms in Japan, reflected in the 6th Basic Plan for scientific, technical and innovative development of the country, on building a society for the fullest realization and use of human intellectual potential.

Office property price index forecasting using neural networks

PurposeThe Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.Design/methodology/approachThe authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.FindingsThe authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.Originality/valueThe results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level

ABSTRACT There is little doubt about importance of forecasting commodity prices to policy makers and diverse varieties of market participants. In this present work, we analyse price forecasting problems for scrap steel for east, north, south, central, northeast, and southwest China and at the national level with daily data during 08/23/2013–04/15/2021. We focus on exploration of usefulness of non-linear auto-regressive neural networks and examine forecasting performance based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data into training, validation, and testing phases. We arrive at relatively simple models for the seven price series, which lead to forecasts of high accuracy and stabilities with relative root mean square errors below 0.85%. Our results could serve as standalone technical forecasts. They could be combined with other forecasts for forming perspectives of price trends and carrying out policy analysis.

Yellow corn wholesale price forecasts via the neural network

PurposeForecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.Design/methodology/approachThe authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.FindingsThe final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.Originality/valueThrough the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

Retail Property Price Index Forecasting through Neural Networks

Rapid growth has been seen in the Chinese housing market during the past decade, and housing price forecasts have become more significant to investors and policy makers. In this study, we explore neural networks for retail property price index forecasts from ten major Chinese cities for July 2005–April 2021. Our goal is to build simple and accurate neural networks to contribute to purely technical forecasts of the Chinese retail property market. To facilitate the analysis, we examine different model settings based on algorithms, delays, hidden neurons, and data splitting ratios and construct a simple neural network with five delays and two hidden neurons. This leads to a rather stable performance of about 1.6% average relative root mean square error across the ten cities for the training, validation, and testing phases. Results here can be used on a stand-alone basis or combined with fundamental forecasts to form perspectives of retail property price trends and conduct policy analyses.

Canola and soybean oil price forecasts via neural networks

Forecasts of commodity prices are vital issues to market participants and policy-makers. Those of cooking section oil are of no exception, considering its importance as one of main food resources. In the present study, we assess the forecast problem using weekly wholesale price indices of canola and soybean oil in China during January 1, 2010–January 3, 2020, by employing the non-linear auto-regressive neural network as the forecast tool. We evaluate forecast performance of different model settings over algorithms, delays, hidden neurons, and data splitting ratios in arriving at the final models for the two commodities, which are relatively simple and lead to accurate and stable results. Particularly, the model for the price index of canola oil generates relative root mean square errors of 2.66, 1.46, and 2.17% for training, validation, and testing, respectively, and the model for the price index of soybean oil generates relative root mean square errors of 2.33, 1.96, and 1.98% for training, validation, and testing, respectively. Through the analysis, we show usefulness of the neural network technique for commodity price forecasts. Our results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

With sincere concern for the Fatherland: M. O. Menshikov's journalism on the development of aeronautics

The subject of the study is the texts of the Russian publicist Mikhail Osipovich Menshikov on the development of domestic aeronautics, considered in the light of heterogeneous assessments of his work. The object is a reflection on the development of domestic aeronautics in the work of the famous Russian journalist of the early twentieth century M. O. Menshikov. The author considered various assessments of the work of the publicist. The most important part of the research is the analysis of M. O. Menshikov's journalistic texts on the development of aeronautics. Considerable attention is paid to the works of the publicist of the period of the Russian-Japanese war and the last years before the First World War, where the bureaucratic activities of officials are critically evaluated. The problems of timely supply of the army, flight safety on the first airplanes, and the technical lag of the Russian Empire from the West were also touched upon. The main conclusions of the work carried out are the assertion of the immutability of Menshikov's patriotic, state-oriented position regarding the development of domestic aeronautics and its ability to technical forecasting. In addition, specific "weak points" in the creative heritage of the publicist were identified: passion for an idea and lack of systematic university education. A special contribution of the author to the study of the topic is a comprehensive analysis of M. O. Menshikov's journalistic texts in the historical aspect. The novelty of the research lies in the identification of a combination of patriotic pathos and high idealism and the above shortcomings in the works of the publicist on aeronautics. The analysis of the texts of the chosen topic (the development of domestic aeronautics), covering the period from 1894 to 1918, showed that Menshikov's accusations of renegade and empty talk are untenable.

Thermal coal price forecasting via the neural network

• We build a non-linear auto-regressive neural network model for forecasts of thermal coal prices. • Closing prices recorded on a daily basis of thermal coal traded in China Zhengzhou Commodity Exchange are used for analysis. • The model is simple and leads to performance of good accuracy and stabilities. • The model could serve as technical forecast tools and for policy analysis. Thermal coal price forecasts represent an essential issue to investors and policy makers, given its importance as a strategic energy source. The current work aims at exploring usefulness of non-linear auto-regressive neural networks for this forecast problem based upon a data-set of closing prices recorded on a daily basis of thermal coal traded in China Zhengzhou Commodity Exchange during January 4, 2016 – December 31, 2020, which is an important financial index not sufficiently explored in the literature in terms of its price forecasts. Through testing a variety of model settings over algorithms, delays, hidden neurons, and data splitting ratios, the model that produces performance of good accuracy and stabilities is reached. Particularly, the model has five delays and ten hidden neurons and is constructed with the Levenberg-Marquardt algorithm based on the ratio of 80%–10%–10% of the data for training–validation–testing. It leads to relative root mean square errors of 1.48%, 1.49%, and 1.47% for the training, validation, and testing phases, respectively. Usefulness of neural networks for the price forecast issue of thermal coal is demonstrated. Forecast results here could serve as standalone technical forecasts and be combined with other forecasts when conducting policy analysis that involves forming perspectives of trends in prices.

Soybean and Soybean Oil Price Forecasting through the Nonlinear Autoregressive Neural Network (NARNN) and NARNN with Exogenous Inputs (NARNN–X)

• We develop nonlinear autoregressive neural network (with exogenous inputs) models for daily agricultural commodity price forecasting. • We explore the forecasting issue for soybeans and soybean oil over a period of greater than fifty years. • We construct simple, accurate, and stable models for the two agricultural commodities of economic significance. • The models are useful as technical forecasting tools and for policy analysis. Price forecasting is a key concern for market participants in the agriculture sector. This study explores usefulness of the nonlinear autoregressive neural network (NARNN) and NARNN with exogenous inputs (NARNN–X) for forecasting issues in data sets of daily prices over periods of greater than fifty years for soybeans and soybean oil. The exogenous input in the NARNN–X for prices of soybeans (soybean oil) are prices of soybean oil (soybeans). Through investigating various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, models resulting in accurate and stable performance for these two commodities are arrived at. The overall relative root mean square errors based on the chosen NARNNs (NARNN–Xs) are 1.701% (1.695%) and 1.777% (1.775%) for soybeans and soybean oil, respectively. Usefulness of the machine learning approach for price forecasting issues of the two commodities is demonstrated, as well as potential usefulness of prices of closely related commodities for providing predictive content. Results here could be used on a standalone basis as technical forecasts or combined with fundamental forecasts for forming perspectives of price trends and conducting policy analysis. The empirical framework here should not be difficult to implement, which is an important consideration to many decision makers, and has potential to be generalized for forecasting prices of other commodities.

Technical Analysis Forecasting and Evaluation of Stock Markets: The Probabilistic Recovery Neural Network Approach

Technical Analysis Forecasting and Evaluation of Stock Markets: The Probabilistic Recovery Neural Network Approach

Engineering strategies and opportunities of next generation biofuel from microalgae: A perspective review on the potential bioenergy feedstock

The quest for alternate fuels that demonstrate reduced environmental impact is ever-increasing. This necessity has been a direct result of the environmental changes that are caused by the usage of fossil fuels. In this regard, sugar cane, corn and soybeans have been employed to make the first-generation biofuels. Second-generation biofuels, made from lignocellulosic crops and forest leftovers, require a lot of land that could have otherwise been utilized for food processing and productions. In this scenario, going by the technical forecasts, microalgae can potentially serve as the primary source for the third-generation of biofuels. By virtue of possessing no such limitations, microalgae are currently viewed as an effective alternative to first- and second-generation biofuels. The goal of this review is to highlight the most effective opportunities for biofuels production using algal sources. In doing so, this review also explains how the chemical compositions of micro and macroalgae, as well as the associated bioactive compounds, render positive influence on biofuel production. In addition to the present review critical discussions have been done on: (a) various growing strategies for rapid cultivation of micro and macroalgae for biomass development, (b) how algal-based ingredients and bioactive compounds are yet unexploited for bioenergy generation, and (c) why algal source and their biomass are regarded as possible bioenergy sources for future green and sustainable energy.

Rent index forecasting through neural networks

PurposeChinese housing market has been growing fast during the past decade, and price-related forecasting has turned to be an important issue to various market participants, including the people, investors and policy makers. Here, the authors approach this issue by researching neural networks for rent index forecasting from 10 major cities for March 2012 to May 2020. The authors aim at building simple and accurate neural networks to contribute to pure technical forecasting of the Chinese rental housing market.Design/methodology/approachTo facilitate the analysis, the authors examine different model settings over the algorithm, delay, hidden neuron and data spitting ratio.FindingsThe authors reach a rather simple neural network with six delays and two hidden neurons, which leads to stable performance of 1.4% average relative root mean square error across the ten cities for the training, validation and testing phases.Originality/valueThe results might be used on a standalone basis or combined with fundamental forecasting to form perspectives of rent price trends and conduct policy analysis.

House price forecasting with neural networks

• We develop neural network models for house price forecasting in China. • We explore the forecasting issue for 100 major Chinese cities. • We construct simple, accurate, and stable models. • The models are useful as technical tools and for policy analysis. The house market has been rapidly growing for the past decade in China, making price forecasting an important issue to the people and policy makers. We approach this problem by exploring neural networks for forecasting of house prices from one hundred major cities for the period of June 2010–May 2019, serving as the first study with such wide coverage for the emerging Chinese market through a machine learning technique. We aim at constructing simple and accurate neural networks as a contribution to pure technical forecasting of house prices. To facilitate the analysis, we investigate different model settings over the algorithm (the Levenberg-Marquardt, scaled conjugate gradient, and Bayesian regularization), delay (from two to six), hidden neuron (two, three, five, and eight), and data spitting ratio (70%–15%–15%, 60%–20%–20%, and 80%–10%–10% for trainingvalidationtesting), and arrive at a rather simple neural network with only four delays and three hidden neurons that leads to stable performance of 1% average relative root mean square error across the one hundred cities for the training, validation, and testing phases. We demonstrate the usefulness of the machine learning approach to the house price forecasting problem in the Chinese market. Our results could be used on a standalone basis or combined with fundamental forecasting in forming perspectives of house price trends and conducting policy analysis. Our empirical framework should not be difficult to deploy, which is an important consideration to many decision makers, and has potential to be generalized for house price forecasting of other cities in China.