Modeling and Forecasting of Agricultural Commodity Production under Changing Climatic Condition: A Review

India is a country with an agrarian economy in which majority of its population rely on agriculture directly as their source of livelihoof. Climate has a very significant role in agricultural production. It predominantly influences growth of the crop, development of the crop and eventually crop yield. Climate also significantly influences the outbreak of disease and pest; it affects the requirement of water by the crop. Possible changes in weather factors, like precipitation, temperature and CO2 concentration are expected to have a significant impact on crop growth. If farmers are able to predict the weather activities and are aware of the effect of these activities on crop production, then it will be beneficial to them as a feasible plan can be devised synchronizing the crop production activities as per changes in the climatic conditions. In view of tackling the aforementioned problem, this article describes various statistical techniques that can play a crucial role in forecasting production of agricultural commodities changing climatic conditions.

Application of the Autoregressive Integrated Moving Average Exogenous (ARIMAX) with Calendar Variation Effect Method for Forecasting Chocolate Data in Indonesia and the United States

Forecasting chocolate consumption is required by producers in preparing the amount of production each month. The tradition of Valentine, Christmas and Eid al-Fitr which are closely related to chocolate makes it impossible to predict chocolate by using the Classical Time Series method. Especially for Eid al-Fitr, the determination follows the Hijri calendar and each year advances 10 days on the Masehi calendar, so that every three years Eid al-Fitr will occur in a different month. Based on this, the chocolate forecasting will show a variation calendar effect. The method used in modeling and forecasting chocolate in Indonesia and the United States is the ARIMAX (Autoregressive Integrated Moving Average Exogenous) method with Calendar Variation effect. As a comparison, modeling and forecasting are also carried out using the Naïve Trend Linear, Naïve Trend Exponential, Double Exponential Smoothing, Time Series Regression, and ARIMA methods. The ARIMAX method with Calendar Variation Effect produces a very precise MAPE value in predicting chocolate data in Indonesia and the United States. The resulting MAPE value is below 10 percent, so it can be concluded that this method has a very good ability in forecasting.

Modeling and forecasting the influence of social infrastructure development of interregional migration in Russia

At present demographic development of Russia and its regions is one of the most important factors of the country’s economic growth and also is key priority of the state government system. In spite of that, the issues of interregional migration are poorly represented in the strategic planning documents both at the federal and regional level. The major migration-induced population growth can be observed in the federal cities, capital areas and central part of Russia. At the same time in 2019 there was migration loss in 47 subjects of the Russian Federation. In many regions it is combined with the natural population loss aggravating the situation. The emerging closed loop (decreasing attractiveness of the area – migration loss – deteriorating social and economic situation – decreasing attractiveness of the area) results in increasing contradictions. Variety of factors determining the direction and the dynamics of migration flows requires a comprehensive study of these processes. The analysis conducted revealed that the level of development of the territory’s infrastructure is of great significance in this matter. Therefore, this direction is considered to be the main one. For quantification the authors established a complex of individual indicators which were preprocessed and consistently merged into directional integral and later into a single integral indicator of the level of social infrastructure development. Moreover, it is advisable to define five groups of regions according to the degree of their potential’s realization and to consider the affiliation of the subject of the federation to a particular group as an additional factor. Thus, the authors obtained the regression equation which describes the interrelation between the parameters under study. ANOVA revealed the opportunity of its practical application. Based on this model, a scenario forecast for the development of social infrastructure in Russia and its regions has been formed.

Modeling and Forecasting Cryptocurrency Closing Prices with Rao Algorithm-Based Artificial Neural Networks: A Machine Learning Approach

Artificial neural networks (ANNs) are suitable procedures for predicting financial time series (FTS). Cryptocurrencies are good investment assets; therefore, the effective prediction of cryptocurrencies has become a trending area of research. Capturing inherent uncertainties associated with cryptocurrency FTS with conventional methods is difficult. Though ANNs are the better alternative, fixing the optimal parameters of ANNs is a tedious job. This article develops a hybrid ANN through Rao algorithm (RA + ANN) for the effective prediction of six popular cryptocurrencies such as Bitcoin, Litecoin, Ethereum, CMC 200, Tether, and Ripple. Six comparative models such as GA + ANN, PSO + ANN, MLP, SVM, LSE, and ARIMA are developed and trained in a similar way. All these models are evaluated through the mean absolute percentage of error (MAPE) and average relative variance (ARV) metrics. It is found that the proposed RA + ANN generated the lowest MAPE and ARV values, statistically different as compared with existing methods mentioned above, and hence can be recommended as a potential financial instrument for predicting cryptocurrencies.

Modeling the volatity of cryptocurrency markets

The application of the model of geometric Brownian motion (GBM) for the problem of modeling and forecasting prices for cryptocurrencies is analyzed. For prediction the solution of the stochastic differential equation of the GBM model is used, which has a linear drift and diffusion coefficients. Different scenarios of price movement are considered. Keywords: geometric Brownian motion (GBM), modeling, forecasting, cryptocurrency.

Vector Autoregressive Modeling of COVID-19 Incidence Rate in Nigeria

With the outbreak of COVID-19, a lot of studies have been carried out in various science disciplines to either reduce the spread or control the increasing trend of the disease. Modeling the outbreak of a pandemic is pertinent for inference making and implementation of policies. In this study, we adopted the Vector autoregressive model which takes into account the dependence that exists between both multivariate variables in modeling and forecasting the number of confirmed COVID-19 cases and deaths in Nigeria. A co-integration test was carried out prior to the application of the Vector Autoregressive model. An autocorrelation test and a test for heteroscedasticity were further carried out where it was observed that there exists no autocorrelation at lag 3 and 4 and there exists no heteroscedasticity respectively. It was observed from the study that there is a growing trend in the number of COVID-19 cases and deaths. A Vector Autoregressive model of lag 4 was adopted to make a forecast of the number of cases and death. The forecast also reveals a rising trend in the number of infections and deaths. The government therefore needs to put further measures in place to curtail the spread of the virus and aim towards flattening the curve.

Animals as objects of civil rights: current issues

The relevance of the research is based on the heated discussion that has unfolded in recent years in connection with changes of the current legislation on legal regime of animals as objects of civil rights as well as awkward suggestions aimed at essentially reshaping the civilistic concept of animals and establishing their special legal status by recognizing them, albeit with some restrictions, as subjects of legal rights. The purpose is to analyze the genesis of animals legislation, including but not limited to international legislation, and to reveal the social significance of norms governing the conditions and procedure for acquisition of animals and the limits and principles of their treatment. The article also aims at voicing the authors position on participation in the civil circulation of animals. Research methods applied in the work are as follows: formal-legal, dialectical unity, system analysis, interpretation, modeling, and forecasting. The results of the study (conclusions) are realized in proposing to supplement Art. 128 of the Civil Code of the Russian Federation with a new term property as basic in relation to terms things, other property, and property rights. It is also recommended to expand the range of objects of civil rights by identifying animals as an independent object, clarify the revision of Art. 137 of the Civil Code, presenting in it the definition of an animal as an object of civil rights and reflecting the main criterion for classifying animals (turnover). In addition, a judgment was made on changes in Russian legislation introduced in 2020, including the Law On the Animal World, allowing amateur and sports hunting of animals in semi-free conditions and artificially created habitat. This law clearly contradicts international agreements that allow hunting (capture) of animals only for the maintenance of human livelihood.