Statistical Forecasting Research Articles

Accurate statistical seasonal streamflow forecasts developed by incorporating remote sensing soil moisture and terrestrial water storage anomaly information

Water managers need improved seasonal streamflow forecasts in drought-prone regions like the U.S. Great Plains, where the inter-annual variability in rainfall and streamflow is high and seasonal streamflow forecasting systems are not well developed. Recent research has demonstrated that in-situ soil moisture measurements can be used to produce accurate streamflow forecasts in the Great Plains where rainfall runoff is the predominant source of streamflow. However, an inadequate density of in-situ soil moisture monitoring locations makes producing such forecasts impossible in many locations. Therefore, our objective was to develop and quantify the accuracy of seasonal streamflow forecasts for rainfall-dominated watersheds using remotely sensed data. Precipitation and remotely sensed soil moisture and terrestrial water storage anomaly data were incorporated into a multiple linear regression (MLR) model to produce seasonal streamflow forecasts for five watersheds in the Great Plains. The best MLR models had Nash-Sutcliffe Efficiency (NSE) values ranging from 0.71 to 0.93, indicating good to very good model performance. In one watershed with nearby in-situ soil moisture data, the use of remotely sensed soil moisture data led to greater forecast accuracy than did the inclusion of the sparse in-situ data. Our results show that the use of remote sensing data in MLR models can provide accurate seasonal streamflow forecasts in rainfall-dominated regions which currently lack such information.

ANALISIS PERAMALAN EKSPOR BESI ATAU BAJA DARI INDONESIA KE SINGAPURA DENGAN METODE TIME SERIES

Exporting iron or steel is a crucial sector for Indonesia's economy, and Singapore is one of its main trading partners in terms of exporting these products. In order to optimize the performance of iron or steel exports to Singapore, a deep understanding of the trends and patterns present in export data is necessary. Therefore, forecasting analysis using time series methods is highly relevant and required. The methodology used in this study is a descriptive-analytical method with data collection from secondary sources. The population consists of iron or steel exports from Indonesia to Singapore, with a sample comprising export data from 2017 to 2021, and the sample determination technique uses the purposive sampling method. The data analysis techniques used are descriptive statistical methods and time series forecasting. The research results indicate that the export of iron or steel from Indonesia to Singapore has shown a positive trend over the past five years, influenced by factors such as strong demand, resource availability, developing infrastructure, product quality, and so on. Based on the forecasting analysis, it is estimated that the demand for iron or steel from Indonesia to Singapore in the coming years will continue to increase. The findings of this research provide valuable insights for the players in the iron or steel industry in Indonesia, especially exporters and stakeholders in the supply chain. With accurate forecasting, production and delivery planning can be optimized, market trends can be identified, and demand fluctuations in Singapore can be anticipated.

Education system and labor market: interdependence issues

Formulation of the problem. The labor market has recently undergone significant changes under the influence of many factors. Among them are globalization, demographic, migration processes, international conflicts and wars, climate change, epidemics, etc. Digitalization and green transit are becoming especially relevant in all spheres of life. This affects the requirements for knowledge and skills of employers who are preparing to receive or change jobs. Therefore, the field of education faces an important task of introducing flexible and effective educational programs. The purpose of the article. To summarize statistical and analythical information and forecasts regarding changes in education in European countries and Ukraine in the context of the transformation of labor market and under the influence of the deployment of industry 4.0. The main results of the study. The article notes the growth of unemployment among young people on the European continent, which began in the early 1960s. Statistics on education and the unemployment rate in Ukraine, damages from the war that was unleashed by Russia in 2022 are given. The growing role of institutions is especially emphasized professional education and training in the general education system of the countries of the world, blurring the boundaries between secondary special and higher education. Two main trends are highlighted in education: the development of information and communication technologies and increasing the role of transversal skills. Statistics on the level of basic digital skills among citizens of EU member states and initiatives to increase the digital literacy of Europeans are given. The presence of different approaches to the ratio of general theoretical and professional disciplines in different national education systems is emphasized. Conclusions. The impact on the sphere of education and the labor market of the process of automation, robotization, and the transition to a green economy remains to be determined. The perspective of introducing integrated approaches in education such as the German "learning fields" (Lernfelder) or the professional "code" in the Finnish system is indicated.

Modeling the Asymmetric Reinsurance Revenues Data using the Partially Autoregressive Time Series Model: Statistical Forecasting and Residuals Analysis

The autoregressive model is a representation of a certain kind of random process in statistics, insurance, signal processing, and econometrics; as such, it is used to describe some time-varying processes in nature, economics and insurance, etc. In this article, a novel version of the autoregressive model is proposed, in the so-called the partially autoregressive (PAR(1)) model. The results of the new approach depended on a new algorithm that we formulated to facilitate the process of statistical prediction in light of the rapid developments in time series models. The new algorithm is based on the values of the autocorrelation and partial autocorrelation functions. The new technique is assessed via re-estimating the actual time series values. Finally, the results of the PAR(1) model is compared with the Holt-Winters model under the Ljung-Box test and its corresponding p-value. A comprehensive analysis for the model residuals is presented. The matrix of the autocorrelation analysis for both points forecasting and interval forecasting are given with its relevant plots.

The Quagmire of Arrested Development in Tropical Cyclones

Abstract The 48-h intensity forecasts for Hurricane Pamela (2021) from numerical weather prediction models, statistical–dynamical aids, and forecasters were a major forecast bust with Pamela making landfall as a minor rather than major hurricane. From the satellite presentation, Pamela exhibited a symmetric pattern referred to as central cold cover (CCC) in the subjective Dvorak intensity technique. Per the technique, the CCC pattern is accompanied by arrested development in intensity despite the seemingly favorable convective signature. To understand forecast uncertainty during occurrences, central cold cover frequency from 2011 to 2021 is documented. From these cases, composites of longwave infrared brightness temperatures from geostationary satellites for CCC cases are presented, and the surrounding tropical cyclone large-scale environment is quantified and compared with other tropical cyclones at similar latitudes and intensities. These composites show that central cold cover has a consistent presentation, but varies in the preceding hours for storms that eventually intensify or weaken. And, the synoptic-scale environment surrounding the tropical cyclone thermodynamically supports the vigorous deep convection associated with CCC. Finally, intensity forecast errors from numerical weather prediction models and statistical–dynamical aids are examined in comparison to similar tropical cyclones. This work shows that guidance struggles during CCC cases with intensity errors from these models being in the lowest percentiles of performance, particularly for 24- and 36-h forecasts. Significance Statement The appearance of symmetric cold clouds near the center of developing tropical cyclones is most often associated with future intensification. This simple relationship is widely used by statistical tropical cyclone intensity forecast models. Here, we reexamine and confirm that one subjectively determined nighttime cold cyclone cloud pattern termed the “central cold cover” pattern in Vern Dvorak’s seminal technique for estimating tropical cyclone intensity from infrared satellite images is indeed related to slow or arrested development, and represents a failure mode for these simple forecast models.

Analysis and evaluation of the bioclimatic productivity of agricultural land in Samara region

The article substantiates the need to take into account the climatic factor in the structure of the natural agro-potential of the territory and the assessment of agricultural land with the same degree of spatial detail as the soil cover. The method for estimating the standard yield based on a combination of a statistical forecast of production and simulation modeling of MPY (maximum possible yield) for grain crops has been implemented in this work. The paper also contains land appraisal and mesoclimatic zoning of the territory and considers the issues of using the results for assessing the suitability and cost of land, solving problems of land use and land administration.

When and how to adjust statistical forecasts in supply chains? Insight from causal machine learning

ABSTRACT Empirical studies have discovered that most statistical forecasts in supply chains are subjected to judgemental adjustments during a forecast review. Although such a practice requires significant management effort and frequently reduces forecast accuracy, many organisations prefer this approach as part of their Sales & Operations Planning process. This study aims to identify the optimal policy to achieve significant accuracy improvement from forecast review. We focus on a practical situation where managers periodically review forecasts from the statistical software and compare them with judgemental forecasts from the sales and marketing functions. Managers must decide whether to disregard the judgement and continue with the existing forecast or revise the statistical forecast based on the judgement. To determine the best course of action, we conducted a numerical experiment using data from five supply-chain companies wit more than 12,000-point forecasts. The experiment considered three alternative actions: “do-nothing”, “follow the judgement”, and “simple-average”. Using a causal machine learning method, namely a policy tree, we develop a set of decision rules that maximise the expected accuracy gains given the variation in forecasting features. The result proposes a simple yet effective policy to recommend suitable actions based on two identified key features: “judgment direction” and the “accuracy of statistical forecasts”. The policy was tested against real-world data and achieved remarkable accuracy with roughly a 3–11 percentage points improvement over the baseline. Our findings offer valuable insights for managers to customise their forecast review policy based on their unique environment.

Statistical Analysis and Forecasting of Indexes of Gender Asymmetry in the Labor Market of the Orenburg Region

All-Russian and regional labor markets operate in an unstable socio-economic situation, which can contribute to the emergence of gender disproportion. The study of gender asymmetry is considered the most popular direction during periods of “economic upheaval”, since this kind of instability increases the inequality of men and women in various spheres of life. The state of turbulence in which the labor market of Russia as a whole and each of its regions is located contributes to the adaptation of each of its segments to new forms of work. This study examines the results of an analysis of the structure and trends in the dynamics of indexes of employment and unemployment of the male and female population in the Orenburg region for 2016-2021. The choice of the time period is justified by the uneven development of both the world and the Russian economy. Modern economic realities, in which the labor market exists, develops both in the country as a whole, and in its regions, experiencing a number of shocks, determine the scientific novelty of the issues under consideration.Purpose of the study. The main idea of the paper done is the possibility of using the methods of economic and statistical analysis to study dynamic differences in gender structures according to various characteristics, development directions that determine gender asymmetry in the labor market of the Orenburg region, and predict its main indexes for the medium term.Materials and methods. The information base of the study was the statistical information of Orenburgstat, which characterizes gender asymmetry in the labor market of the Orenburg region. To achieve this goal, a set of methods of economic and statistical analysis was applied, including the calculation of indexes of the structure and structural differences, dynamics, identifying trends, forecasting, as well as presenting the results of the study using tables and figures.Results. According to the results of the study, an average portrait of a busy and carefree person by gender was compiled. The characteristics of the male and female population in the labor market of the Orenburg region in 2021 compared to 2016 are highlighted. The study of structural differences was carried out according to the criterion of V. Ryabtsev, during which it was noted that there were no significant changes in the structures of employed men and women. A significant level of differences characterizes the age structure of unemployed men and women in 2021 compared to 2016, as well as the structure of unemployed men by level of education. The study made it possible to assert that in the dynamics of indexes characterizing gender inequality in the labor market of the Orenburg region, unstable changes are observed. The study tested the hypothesis of the presence/absence of trends in the time series of indexes characterizing gender asymmetry in the labor market of the region. For this purpose, one of the modifications of the series criterion was used. The assumption that there is no trend in the dynamics series under consideration has not been confirmed, therefore, the trend exists. Taking into account this circumstance, trend models were created, among which, according to the best statistical characteristics, second-order polynomial models were selected. With the help of the selected polynomials of the second degree, it was determined that in the dynamics of employment and unemployment indexes of the male and female population, downward trends of change prevail. Further, their prospective indexes were calculated. Conclusion. The study made it possible to analyze the main vectors that are emerging in the field of employment and unemployment in the regional labor market by gender, and to predict their main indexes for the medium term. In the dynamics of the number of both male and female labor force in the Orenburg region, unstable trends will be observed in the future. The number of women employed in the region’s economy will grow; the number of unemployed women will decrease. In the forecast period, the number of employed men in the economy of the Orenburg region will change under the influence of a downtrend. The number of unemployed men in the period from 2023 to 2025 will tend to decrease.

Hybrid Dynamical–Statistical Forecasts of the Risk of Rainfall in Southeast Asia Dependent on Equatorial Waves

Abstract Equatorial waves are a major driver of widespread convection in Southeast Asia and the tropics more widely, a region in which accurate heavy rainfall forecasts are still a challenge. Conditioning rainfall over land on local equatorial wave phases finds that heavy rainfall can be between 2 and 4 times more likely to occur in Indonesia, Malaysia, Vietnam, and the Philippines. Equatorial waves are identified in a global numerical weather prediction ensemble forecast [Met Office Global and Regional Ensemble Prediction System (MOGREPS-G)]. Skill in the ensemble forecast of wave activity is highly dependent on region and time of year, although generally forecasts of equatorial Rossby waves and westward-moving mixed Rossby–gravity waves are substantially more skillful than for the eastward-moving Kelvin wave. The observed statistical relationship between wave phases and rainfall is combined with ensemble forecasts of dynamical wave fields to construct hybrid dynamical–statistical forecasts of rainfall probability using a Bayesian approach. The Brier skill score is used to assess the skill of forecasts of rainfall probability. Skill in the hybrid forecasts can exceed that of probabilistic rainfall forecasts taken directly from MOGREPS-G and can be linked to both the skill in forecasts of wave activity and the relationship between equatorial waves and heavy rainfall in the relevant region. The results show that there is potential for improvements of forecasts of high-impact weather using this method as forecasts of large-scale waves improve.

Improving the accuracy of a weighing scale by rigorously combining measurements

In this paper, we investigate the question of how much measurement accuracy of additive quantities can be increased by the method of combined measurements. Therefore, we consider measurements of all possible combinations of n labeled masses and then estimate the individual weights of the n masses by a linear regression approach. We present experimental results which motivate comprehensive simulation campaigns. These simulations provide valid statistical statements and reliable forecasts of the experimental results. A profound analytical treatment, in turn, supports these simulation outcomes with excellent consistency. One important achievement therein is a general analytical expression for the accuracy that can be achieved by combined measurements. It turns out this method allows to estimate the weight of mass elements with an accuracy that potentially under-runs by orders of magnitude the resolution of the scale used. In a broader sense, our work wants to promote the method and give inspirations to applications in various metrological fields beyond high-precision mass determination.

Statistical Analysis and Forecasting of Gender Asymmetry Indexes in the Labor Market of the Orenburg Region

The labor market in Russia and its regions operates in an unstable socio-economic situation, which can contribute to the emergence of gender disproportion. The study of gender asymmetry is considered the most popular direction during periods of “economic shocks”, since this kind of instability increases the inequality of men and women in various spheres of life. The state of turbulence in which the labor market of Russia as a whole and each of its regions is located contributes to the adaptation of each of its segments to new forms of work. This study examines the results of an analysis of the structure and trends in the dynamics of indexes of employment and unemployment of the male and female population in the Orenburg region for 2016-2021. The choice of the time frame is justified by the uneven development of both the world and the Russian economy. Modern economic realities, in which the labor market exists and develops as a country as a whole and its regions, experiencing a number of shocks, determine the scientific novelty of the issues under consideration.Purpose of the study. The main idea of the work done is the possibility of using the methods of economic and statistical analysis to study dynamic differences in gender structures according to various characteristics, development directions that determine gender asymmetry in the labor market of the Orenburg region, and predict its main indexes for the medium term.Materials and methods. The information base of the study was the statistical information of Orenburgstat, which characterizes gender asymmetry in the labor market of the Orenburg region. To achieve this goal, a set of methods of economic and statistical analysis was applied, including the calculation of indexes of the structure and structural differences, dynamics, identifying trends, forecasting, as well as presenting the results of the study using tables and figures.Results. According to the results of the study, an average portrait of a busy and carefree person by gender was compiled. The characteristics of the male and female population in the labor market of the Orenburg region in 2021 compared to 2016 are highlighted. The study of structural differences was carried out according to the criterion of V. Ryabtsev, during which it was noted that there were no significant changes in the structures of employed men and women. A significant level of differences characterizes the age structure of unemployed men and women in 2021 compared to 2016, as well as the structure of unemployed men by level of education.The study made it possible to assert that in the dynamics of indexes characterizing gender inequality in the labor market of the Orenburg region, unstable changes are observed. The study tested the hypothesis of the presence/absence of trends in the time series of indexes characterizing gender asymmetry in the labor market of the region. For this purpose, one of the modifications of the series criterion was used. The assumption that there is no trend in the dynamics series under consideration has not been confirmed, therefore, the trend exists. Taking into account this circumstance, trend models were constructed, among which, according to the best statistical characteristics, secondorder polynomial models were selected. With the help of the selected polynomials of the second degree, it was determined that in the dynamics of employment and unemployment indexes of the male and female population, downward trends of change prevail. Further, their prospective indexes were calculated.Conclusion. The study made it possible to analyze the main vectors that are emerging in the field of employment and unemployment in the regional labor market by gender, and to predict their main indexes for the medium term. In the dynamics of the number of both male and female labor force in the Orenburg region, unstable trends will be observed in the future. The number of women employed in the region’s economy will grow; the number of unemployed women will decrease. In the forecast period, the number of employed men in the economy of the Orenburg region will change under the influence of a downtrend. The number of unemployed men in the period from 2023 to 2025 will tend to decrease.

Emerging opportunities for hybrid perovskite solar cells using machine learning

While there are several bottlenecks in hybrid organic–inorganic perovskite (HOIP) solar cell production steps, including composition screening, fabrication, material stability, and device performance, machine learning approaches have begun to tackle each of these issues in recent years. Different algorithms have successfully been adopted to solve the unique problems at each step of HOIP development. Specifically, high-throughput experimentation produces vast amount of training data required to effectively implement machine learning methods. Here, we present an overview of machine learning models, including linear regression, neural networks, deep learning, and statistical forecasting. Experimental examples from the literature, where machine learning is applied to HOIP composition screening, thin film fabrication, thin film characterization, and full device testing, are discussed. These paradigms give insights into the future of HOIP solar cell research. As databases expand and computational power improves, increasingly accurate predictions of the HOIP behavior are becoming possible.

Skillful U.S. Soy Yield Forecasts at Presowing Lead Times

Abstract Soy harvest failure events can severely impact farmers, insurance companies, and raise global prices. Reliable seasonal forecasts of misharvests would allow stakeholders to prepare and take appropriate early action. However, especially for farmers, the reliability and lead time of current prediction systems provide insufficient information to justify within-season adaptation measures. Recent innovations increased our ability to generate reliable statistical seasonal forecasts. Here, we combine these innovations to predict the 1–3 poor soy harvest years in the eastern United States. We first use a clustering algorithm to spatially aggregate crop producing regions within the eastern United States that are particularly sensitive to hot–dry weather conditions. Next, we use observational climate variables [sea surface temperature (SST) and soil moisture] to extract precursor time series at multiple lags. This allows the machine learning model to learn the low-frequency evolution, which carries important information for predictability. A selection based on causal inference allows for physically interpretable precursors. We show that the robust selected predictors are associated with the evolution of the horseshoe Pacific SST pattern, in line with previous research. We use the state of the horseshoe Pacific to identify years with enhanced predictability. We achieve high forecast skill of poor harvests events, even 3 months prior to sowing, using a strict one-step-ahead train-test splitting. Over the last 25 years, when the horseshoe Pacific SST pattern was anomalously strong, 67% of the poor harvests predicted in February were correct. When operational, this forecast would enable farmers to make informed decisions on adaption measures, for example, selecting more drought-resistant cultivars or change planting management. Significance Statement If soy farmers would know that the upcoming growing season will be hot and dry, they could decide to take anticipatory action to reduce losses, that is, buy more drought resistant soy cultivars or change planting management. To make such decisions, farmers would need information even prior to sowing. On these very long lead times, a predictable signal can emerge from low-frequency processes of the climate system that can affect surface weather via teleconnections. However, traditional forecast systems are unable to make reliable predictions at these lead times. In this work, we used machine learning techniques to train a forecast model based on these low-frequency components. This allowed us to make reliable predictions of poor harvest years even 3 months prior to sowing.

Spatiotemporal estimation of analysis errors in the operational global data assimilation system at the China Meteorological Administration using a modified SAFE method

AbstractQuantification of the uncertainties in initial analyses against the real atmosphere (“reality”) provides a fundamental reference for the evaluation and development of operational data assimilation (DA) systems. Due to the unknown reality, most existing methods for analysis error estimation use reanalysis datasets or observations as a proxy for reality, which are empirical, non‐objective, and biased. Unlike these methods, our study adopted a modified Statistical Analysis and Forecast Error (SAFE) estimation method to objectively and directly quantify spatiotemporal errors in analyses compared to reality based on unbiased assumptions. In the present study, the SAFE method was first applied to estimate the annual variation and spatial distribution of analysis errors in the Global Forecast System of Global/Regional Assimilation and PrEdiction System (GRAPES_GFS) at the China Meteorological Administration (CMA) since the beginning of its operational implementation (i.e., 2016–2021). Qualitative comparison to analysis error estimations in previous studies showed that SAFE can provide more reasonable spatial‐mean analysis error profiles than can the estimation with the ERA‐5 reanalysis as a reference (the approach hereafter called “ERAv”). Moreover, ERAv overestimates (underestimates) the spatial‐mean analysis error below (above) approximately 500 hPa compared to SAFE because it neglects the uncertainties inherent in reanalysis. Overall, the SAFE estimation reveals that relative reductions of about 12.5%, 29%, and 24.5% were achieved for the spatial‐mean analysis errors of wind, temperature, and geopotential height, respectively, in the GRAPES_GFS throughout the six‐year study period. These results can largely be attributed to the DA scheme being upgraded from 3D‐Var to 4D‐Var. SAFE can also provide more reasonable and accurate pointwise analysis errors than ERAv can.

Análisis de los inventarios a través de un modelo de pronósticos estadísticos en una empresa peruana

The importance of the study falls on the main objective of analyzing inventories through statistical forecasts in a pharmaceutical company. The research approach was quantitative, cross-sectional, non-experimental design and descriptive level. The study population was the information from the inventory of the company for the year 2022, likewise the sample consisted of two products classified in item A. According to what was elaborated in the article, it is concluded that the products selected in group A, they have a 15% participation in the company's inventories, however, they represent 86.6% according to their unit cost and 45.3% of inventory valuation according to their value, also according to their profit contribution they have a 49.76% participation. And, according to the analysis of the forecasting methods, it was determined that the ideal for both products 002 and 005 is the seasonal index method, considering the MAPE (average absolute percentage error) lower, reaching 28.96% and 27.28% respectively. The economic order quantity was determined where it was seen that for product 002 it is 50392 and product 005 is 73217.

Incomes of the Population of Kazakhstan: Differentiation and Forecast

The purpose of the study is to forecast and determine the main factors affecting the growth of per capita income of the population of Kazakhstan. The paper is devoted to an urgent problem – differentiation and forecast of the income level of the population of Kazakhstan. In the study, based on the results of a survey conducted as part of the study in 2021, 4 conditionally defined social groups were formed according to the subjective integral assessment of respondents' own income. Based on the formed decile groups of respondents, the Gini coefficient was calculated and compared with official statistics. The method of statistical forecasting was used in the preparation of the article. With the help of factor forecasting, the system of the interrelation of per capita monetary incomes of the population with other factors is modelled. In the article, when finding factors affecting the incomes of the population of Kazakhstan, a model was obtained in which six factors turned out to be statistically significant: Three indicators of them, these are the Minimum wage, the Minimum pension and Subsistence minimum, are state minimum social standards. It is obvious that it is necessary to revise the indicators on the value of which the level and quality of life of the population and the economic growth of the country depend. In further studies, based on the results obtained, it is planned to develop recommendations for reducing income inequality in Kazakhstan.

Intra-hour PV power forecasting based on sky imagery

This paper introduces an upgraded version of the PV2-state model [Paulescu et al. Renew Energy 195 (2022) 322] for intra-hour photovoltaic (PV) power forecasting. The model incorporates real-time adjustments to the estimated clear-sky PV power or the transmittance of clouds, considering the Sun coverage by clouds. A physics-based approach for processing cloud field information from an all-sky imager is proposed to intra-hour forecast SSN, a binary quantifier stating whether the Sun is shining (SSN = 1) or not (SSN = 0). The model performance with the new procedure is investigated from three perspectives: forecast accuracy, forecast precision and the response to the variability in the state-of-the-sky. The study was conducted with high-quality 1-min data collected from a micro-PV plant, on a sample of days with scattered clouds. PV2-state demonstrates notable performance particularly under challenging conditions, where models typically struggle to perform well. In terms of skill score, the overall model performance ranges between 10 and 20%, and under very high variability conditions even exceeding four times the purely statistical forecast. For the operationally relevant 15-min horizon, PV2-state exhibits a noteworthy precision, with two-thirds of forecasts falling within a 10% tolerance interval. Even under severe conditions roughly one third falls in this 10% interval.

TOURISM AS A DRIVER OF SOCIO-ECONOMIC AND SOCIO-CULTURAL DEVELOPMENT OF THE COUNTRY

The relevance of the studied area is due to the need for special attention of state authorities and local governments to the development of tourism, since with an effective process of managing the development of this area in a certain area, the latter can receive a significant positive socio-economic effect. The aim of the article is to study the significance of tourism development for socio-economic systems. The article defines mutually influencing factors on the development of tourism and the country; macroeconomic indicators of tourism activities of the top 5 countries of the world for 2015 and 2017 are given, according to the latest data from experts from the World Economic Forum; the final part offers recommendations aimed at increasing the volume of tourist services. The methodological basis of the study is presented by the methods of scientific generalization, comparative analysis, complex, systemic and situational approaches. The use of UN and UNWTO statistical standards makes it possible to conduct more complete and comparable statistical studies, and also to make statistical forecasts for the development of tourism as an integral and important part of the economy of any region. The rating will become tourism on the international level in different countries on the basis of the System of National Rakhunkiv (SNR), the rating of the country in terms of competitiveness will be more expensive All-World Tourism Organization (UNWTO), as the world's largest international organization for tourism, which has the status of a specialized establishment of the UN.
 UNWTO (United Nations WorldTourism Organization) is engaged in the development of international and globally accessible tourism.
 Within the framework of the economy of various regions of the world in modern conditions, tourism plays an increasingly important role. However, not all regions are able to ensure the long-term sustainable development of tourism on their territory. One of the main causes of problems in the development of regional tourism is the imperfection of the regional tourism management system. This article discusses the main issues related to the impact of tourism on the regional economy, and also proposes a number of measures to reform the management of tourism activities at the regional level.

A Novel First-Order Fuzzy Rules-Based Forecasting System Using Distance Measures Approach for Financial Market Forecasting

The precise estimates about finance, atmospheric science, power sector, industries, agriculture, and other science help governments and institutions economically in making the relevant policies regarding import-export, demand, consumption, storage, and local industries. Due to the uncertainty and nondeterministic behavior of data series with respect to time, the foremost challenge is to develop and identify the practical method to handle the above stated complex issues. As an illustration, this study presented an analysis of a new fuzzy time-series (FTS) approach and comparison with traditional forecasting models for prediction of gram pulse production. Taking into consideration the theory of fuzzy sets, FTS, fuzzy rules, triangular membership functions, distance measures, and modified weighted average method, a robust and effective fuzzy rules-based methodology was developed for the prediction of time-series data regarding crop production and share prices. Conventional statistical forecasting methods such as Holt’s linear trend, Holt’s exponential trend, and Holt’s damped exponential trend models were also applied on time-series data for comparison. To identify the primacy of modeling and forecasting, the techniques of root mean squared error (RMSE) and mean absolute error (MAE) were used as a criterion. The numerical values of RMSE and MAE such as 106.51 and 74.8897 clearly demonstrated that the proposed fuzzy rules-based method is robust for forecasting of production and market share prices in the environment of uncertainty.

Combined Forecasts of Intermittent Demand for Stock-keeping Units (SKUs)

Effective inventory management requires accurate forecasts for stock-keeping units (SKUs), especially for the strategic ones for companies’ operations and after-sales services like providing spare parts. Forecasting is a challenging task for such SKUs as they usually have intermittent demand (ID) patterns, consisting of many periods with zero demand and infrequent demand arrivals. Given the highly uncertain nature of ID for SKUs, this study developed a methodological framework for combining statistical and judgmental forecasts and assessed the performance of the proposed framework by using accuracy and bias measures. The forecasting process has several steps, including data preparation, data categorization based on demand patterns, generating statistical and judgmental forecasts, combining statistical and judgmental forecasts, and evaluating the forecast performance. These steps were illustrated on a real-world dataset that contains monthly customer demand data for after-sales spare parts. Results showed that combination is the best method for the majority of SKUs. This paper contributes to the limited literature by addressing the gap between the combined and ID forecasts. The proposed framework gives practitioners and researchers a comprehensive overview to help them make more accurate forecasts while encouraging the use of simple but structured approaches.