Smoothing Method Research Articles

Bayesian Ensembles of Exponentially Smoothed Life-Cycle Forecasts

Problem definition: We study the problem of forecasting an entire demand distribution for a new product before and after its launch. Firms need accurate distributional forecasts of demand to make operational decisions about capacity, inventory, and marketing expenditures. We introduce a unified, robust, and interpretable approach to producing these pre- and postlaunch distributional forecasts. Methodology/results: Our approach is inspired by Bayesian model averaging. Each candidate model in our ensemble is a life-cycle model fitted to the completed life cycle of a comparable product. A prelaunch forecast is an ensemble with equal weights on the candidate models’ forecasts, whereas a postlaunch forecast is an ensemble with weights that evolve according to Bayesian updating. Our approach is part frequentist and part Bayesian, resulting in a novel approach tailored to the demand forecasting challenge. We also introduce a new type of life-cycle or product diffusion model with states that can be updated using exponential smoothing. The trend in this model follows the density of an exponentially tilted Gompertz random variable. For postlaunch forecasting, this model is attractive because it can adapt itself to the most recent changes in a product’s life cycle. We provide closed-form distributional forecasts from our model. In two empirical studies, we show that when the ensemble’s candidate models are all in our new type of exponential smoothing model, this version of the ensemble outperforms several leading approaches in both point and quantile forecasting. Managerial implications: In a data-driven operations environment, our model can produce accurate forecasts frequently and at scale. When quantile forecasts are needed, our model has the potential to provide meaningful economic benefits. In addition, our model’s interpretability should be attractive to managers who already use exponential smoothing and ensemble methods for other forecasting purposes. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0359 .

Evaluation of the implementation of Main Distribution Material (MDU) spare part procurement at seven customer service unit (ULP) in Semarang are using ABC analysis and time series forecasting

Evaluation of the Implementation of Main Distribution Material (MDU) Spare Part Procurement at Seven Customer Service Units (ULP) in Semarang Area using ABC Analysis and Time Series Forecasting (Case Study: PT PLN (Persero) UP3 Semarang) Time series forecasting is a method used to predict the number of future needs in a time series. This research was conducted at the company PT PLN (Persero) UP3 Semarang, where the author tried to deal with the company's problems related to the procurement of Main Distribution Material (MDU) which often occurs overstock or lack of stock. The author uses ABC analysis to classify goods based on value ratings ranging from the highest value so that three parts with group A are obtained for forecasting, kWH meters, 2 × 10 mm2 PWR cables, and 4A MCBs. Forecasting is done with four methods, the Multiplicative Decomposition method, the Holt-Winters method, the Moving Average method, and the Exponential Smoothing method. From the results of the error calculation, it is obtained that the Holt-Winters method obtains the lowest percentage error with results of 10% - 20% or is good to use for forecasting. The comparison between the forecasting results and the overall actual demand is above 70%, which means that the forecasting results are close to the actual demand that occurs, thus increasing the accuracy of the Holt-Winters forecasting method. Suggestions for improvement that can be given to the company are to apply the Holt-Winters forecasting method in forecasting MDU spare part inventory at PT PLN (Persero) UP3 Semarang.

A New Grey Prediction Model and Its Application in Renewable Energy Consumption

Renewable energy is an energy resource that can be used continuously. At present, international oil prices continue to rise, the problem of global climate change is becoming increasingly prominent, and renewable energy and clean energy have ushered in a new round of development opportunities. Based on the new gray prediction model, this paper forecasts the consumption of renewable energy and further analyzes the sustainable development of renewable energy. In this paper, the combinatorial optimization method of cumulative order, background value coefficient, and initial conditions, parameter optimization combination, parameter combinatorial optimization process of the gray prediction model, and parameter optimization mechanism based on the PSO algorithm are proposed, and the reduction error analysis is carried out. The consumption of wind power and photovoltaic renewable energy is forecasted, and three different forecasting methods as exponential smoothing method, time series analysis method, and new gray forecasting method are compared, and the wind speed, irradiation intensity, and load are forecasted by these three different forecasting methods. Compared with the time series analysis method and the exponential smoothing method, the RMSE of the new grey prediction method is reduced by 127.12% and 160.59%, and the error rate is reduced by 3.16% and 4%, respectively. Based on the consumption forecast of renewable energy, this paper analyzes its sustainability from three directions economy, resource supply, and environment, and finally gives energy policy recommendations.

Boosted Whittaker–Henderson Graduation

The Whittaker–Henderson (WH) graduation is a smoothing method for equally spaced one-dimensional data such as time series. It includes the Bohlmann filter, the Hodrick–Prescott (HP) filter, and the Whittaker graduation as special cases. Among them, the HP filter is the most prominent trend-cycle decomposition method for macroeconomic time series such as real gross domestic product. Recently, a modification of the HP filter, the boosted HP (bHP) filter, has been developed, and several studies have been conducted. The basic idea of the modification is to achieve more desirable smoothing by extracting long-term fluctuations remaining in the smoothing residuals. Inspired by the modification, this paper develops the boosted version of the WH graduation, which includes the bHP filter as a special case. Then, we establish its properties that are fundamental for applied work. To investigate the properties, we use a spectral decomposition of the penalty matrix of the WH graduation

Field test and numerical research on explosion crater in calcareous sand

The explosion in foundation poses a significant threat to people and buildings. Currently, a unified empirical prediction formula for crater in calcareous foundation has not been established. In this paper, analyzed the types and sizes of explosion crater with different scaled burial depths through field tests and numerical simulation. In field tests, revealed the influence of scaled burial depth on the type and size of explosion crater and obtained the critical scaled burial depth for three different types of explosion craters, namely ejecta-type crater, collapse-type crater and covert explosion. Through the Smooth Particle Hydrodynamic-Finite Element Method (SPH-FEM) coupling algorithm, studied the movement trajectory of sand particles around the explosive at the moment of explosion in detail. Based on the field tests and numerical simulation results, it was found that calcareous sand has a smaller specific gravity due to its own characteristics, and the size of the explosion crater is larger than that of quartz sand at the same scaled burial depth. Obtained an empirical formula for crater in calcareous sand. Which can quickly predict the size of explosion crater and provide calculation basis for explosion resistant design in calcareous sand foundations.

Nonstationary adaptive S-wave leakage suppression of ocean-bottom node data

Ocean-bottom nodes (OBNs) are widely used because of their wide azimuth, long-offset, and low-frequency advantages. However, in the vertical component of the OBN geophone, a significant amount of S-wave induced noise may be recorded. This significantly impacts the quality of the vertical component data and may affect the follow-up merging of dual-sensor data. Some commercially available methods apply normal-moveout correction, which requires velocity data. We avoid this with an adaptive matching method, which relies solely on seismic data. Therefore, we develop a novel adaptive subtraction method for S-wave leakage suppression using the horizontal component as the noise model. Our method effectively handles the nonstationarity of the input seismic data in all time and space directions, mitigating instability caused by manual selection of the smoothing radius. A method is introduced for estimating a global nonstationary smoothing radius using the noise model. Compared with commercial and stationary smoothing methods, our method can better suppress S-wave noise while balancing residual noise and signal leakage more effectively. Synthetic and field data examples demonstrate significant improvement with our method.

HOLT WINTERS EXPONENTIAL SMOOTHING UNTUK MERAMALKAN PRODUK DOMESTIK BRUTO DI INDONESIA

A country's economic growth will be seen as having grown better or worse than in the past by measuring based on the increase in Gross Domestic Product (GDP). The pattern of Indonesian GDP from 2010 to 2022 shows that the data increases from year to year and there are seasonal fluctuations in the quarter. Holt Winters method is part of the Exponential Smoothing method used for forecasting if the data shows a trend and seasonality in the data pattern. The Holt Winters method has two models, namely additive and multiplicative. Holt Winters Additive is used if the data shows trends and seasonal patterns remain constant. Multiplicative Holt Winter is used if the data shows trends and seasonal patterns proportional to the average rate of the seasonal time series. The data used in this study are GDP Based on Current Prices (Nominal GDP) and GDP on the Basis of Constant Prices (Real GDP). Based on the evaluation of model performance using test data forecasting, the Holt Winters Multiplicative model of Nominal GDP with a MAPE value of 4,767535% is the best model because it has an accuracy value of <10%. While the Holt Winters Additive model of Real GDP with a MAPE value of 4,42387% is also the best model because it has an accuracy value of <10%.

Comparison of Moving Average and Exponential Smoothing Methods for Estimating the Number of Internet Service Disruptions

Fairly tight competition in telecommunication business requires companies to continue improving the quality of their services, including handling network problems or customer complaints. However, the amount of disturbance that occurs is often unpredictable. Indeed, this condition will impact the allocation of resources and budget for handling customer disturbances in the following period, with uncertainty. This research aims to apply the moving average (MA) and single exponential smoothing (SES) forecasting methods to predict the number of internet service disruptions. The case study used in this paper is an internet service provider company with service coverage in the northern part of Surabaya. The number of disturbances data was collected from January 2022 to May 2022, with 18,453 data in total. The disturbances can be divided into five types: physical, mass, logical, PSB/migration, and others. Forecasting is carried out using the MA method with a period of three months. Meanwhile, forecasting using the SES method was carried out by first determining the alpha value that produces the smallest Mean Absolute Percentage Error (MAPE) value. The analysis results show that both forecasting methods are relatively effective and efficient in estimating the number of disturbances. Forecasting performance testing was carried out by measuring the Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE) values. The results of forecasting performance measurements show that the SES method is much better than MA for all types of disturbance data, with MAPE values ​​below 2%.

Improving the estimation of the Boyce index using statistical smoothing methods for evaluating species distribution models with presence‐only data

Species distribution models (SDMs) underpin a wide range of decisions concerning biodiversity. Although SDMs can be built using presence‐only data, rigorous evaluation of these models remains challenging. One evaluation method is the Boyce index (BI), which uses the relative frequencies between presence sites and background sites within a series of bins or moving windows spanning the entire range of predicted values from the SDM. Obtaining accurate estimates of the BI using these methods relies upon having a large number of presences, which is often not feasible, particularly for rare or restricted species that are often the focus of modelling. Wider application of the BI requires a method that can accurately and reliably estimate the BI using small numbers of presence records. In this study, we investigated the effectiveness of five statistical smoothing methods (i.e. thin plate regression splines, cubic regression splines, B‐splines, P‐splines and adaptive smoothers) and the mean of these five methods (denoted as ‘mean') to estimate the BI. We simulated 600 species with varying prevalence and built distribution models using random forest and Maxent methods. For training data, we used two levels for the number of presences (NPtrain: 20 and 500), along with 2 × NPtrain and 10000 random points (i.e. random background sites) for each modelling method. We used the number of presences at four levels (NPbi: 1000, 200, 50 and 10) to investigate its effect, together with 5000 random points to calculate the BI. Our results indicate that the BI estimates from the binning and moving window methods are severely affected by the decrease of NPbi, but all the estimates of the BI from smoothing‐based methods were almost always unbiased for realistic situations. Hence, we recommend these methods for estimating the BI for evaluating SDMs when verified absence data are unavailable.

Research on the path planning integrated with multi-steer modes for heavy reconfigurable unit

Heavy reconfigurable units (HRUs) can form the heavy transportation vehicular platform (HTVP) to perform transportation for large equipment, by combining with each other. With all-wheel-independent-steered, the HRU can realize multi-steer modes (MSMs) such as Ackermann steer mode (ASM), diagonal steering mode (DSM), and zero radius steer mode (ZSM). However, with heavy weight and long wheelbase, the HRU’s mobility is poor if it only employs ASM. Therefore, this paper proposes a hierarchical path planning architecture, realizing path planning integrated with the above steer modes. This architecture includes the MSMs behaviors planning layer (upper layer) and smooth path planning layer (low layer). Firstly, this paper innovative proposes the MSM graph-based search algorithm in the upper layer, which extends nodes through graph-based searching on the grid map considering MSMs information. Then, the upper layer generates MSMs nodes sequence considering improved cost function for MSMs. Secondly, smooth path planning methods for the MSMs are respectively applied to the ASM sequence, DSM sequence, and ZSM sequence. Comparative simulations are shown as results based on measured parameters from real HRU. The results from the proposed method have the lowest or one of the lowest time cost compared with pure ASM planner and special steer modes planner used in the simulations. The maximum optimization rate can reach 10% or more.

Социально-этический маркетинг как инструмент реализации концепции устойчивого развития социально ответственной организации

The issues of using the concept of sustainable development in the management practice of modern enterprises and organizations focused on development and permanent strategic growth are updated. The indicators of sustainable development of an economic entity in the trinity of economic, social and environmental indicators are presented. It is determined that the concept of socio-ethical marketing implemented by the organization as a tool of a socially responsible organization maximally contributes to achieving the goals of a progressive modern concept. The analysis of the tools of social and ethical marketing of the EFKO Group of Companies (EFKO Group) of the Belgorod region, one of the largest food companies in the Russian Federation, a leader in the fat and oil industry market, guided in its activities by the principles inherent in the concept of sustainable development. The dynamics of the main indicators of the Group's economic activity against the background of permanent investments in the implementation of social and ethical activities is presented. The correlation of the value of social investments (expenses for social and ethical marketing) and the value of sales (revenue) of EFKO Group indicates a close relationship between the analyzed indicators. The profit forecast using the exponential smoothing method against the background of growing social investments and social and ethical activity of EFKO Group proves the expediency (including economic) of implementing social and ethical marketing and defines this toolkit as contributing to the growth of the organization's competitiveness not only through active marketing activities, but also through the formation and maintaining a positive image and business reputation of the company; strengthening the image of a socially oriented employer, which is confirmed by the results of independent comparative studies and assessment of the investment and credit attractiveness of companies in the market.

PREDICTION OF TIN EXPORTS, POPULATION, POVERTY, AND LABOR FORCE IN THE PROVINCE OF BANGKA BELITUNG ISLANDS

The COVID-19 virus has also caused shocks to the Bangka Belitung Islands Province in various sectors, especially the economy. To overcome this problem, of course the government has prepared responsive policies, both fiscal and monetary policies to prevent post-COVID-19 risks, especially in the economic recession. To prevent a post-COVID-19 economic recession, a prediction or time series forecast is needed on four variables that influence the economic recession, namely the number of tin exports, population, poverty and labor force in the Bangka Belitung Islands Province so that economic growth is maintained. This research aims to predict the four research variables by comparing the Moving Average and Exponential Smoothing methods. This research also uses Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) as indicators of model accuracy. Based on the results of the accuracy indicators of this model, it was found that the Exponential Smoothing method was better than the Moving Average method. The predicted results for the value of tin exports in 2024 are -3.3645811 with The RMSE value is 42293770, MAE is 29558091, and MAPE is 84.46131. The negative value in the tin export prediction means that the decline in the value of tin exports in 2024 will not have a significant effect because it is still within a reasonable figure. The total labor force in 2024 will be 11057.23 with RMSE value is 16536.48, MAE value is 14194.02, and MAPE is 112.8078. Then for population the predicted result is 21241.92 with RMSE is 19537.82, MAE is 11548.41, and MAPE is 37.51894. Then for the predicted results the number of poverty is 70.22749 with RMSE, MAE, and MAPE respectively of 3992.146, 3205.528, and 139.1129. The alpha value used is 0.0183.

The State and Trends of Crime in the Territory of the Member States of the Commonwealth of Independent States in 2023

Introduction. The article provides an analysis of the state of crime in the member states of the Commonwealth of Independent States in 2023. The analysis is based on statistical data on the state of crime in the Commonwealth over the past five years.After Russia launched a special military operation in 2022 to denazify and demilitarize Ukraine, a situation is developing in the world in which the countries of the collective West are trying to damage our country through economic sanctions, as well as actually waging a proxy war against Russia. Unfriendly countries, led by the United States, are trying to use the member states of the Commonwealth as a tool in the fight against Russia, through unequal cooperation to introduce differences in the CIS, and use information security technologies for their own purposes.To determine the degree of influence of current world events on the criminal situation in the post-Soviet space, an analysis of the state of crime in the CIS countries was carried out.The given statistical data on crime in the Commonwealth countries demonstrate the dynamics of the criminal situation in the countries under consideration, depending primarily on internal criminogenic factors. Such factors exist and develop under the influence of criminal elements and the existing criminal environment.The analysis of the dynamics of the criminal situation for 2019-2023 was aimed at determining the trends of its development in the light of the impact of external and internal processes that have arisen in recent years, to identify the causes affecting the dynamics of certain types of crime and to make a forecast of the possible development of the criminal situation for the next 1-2 years.To predict the dynamics of the criminal situation in the CIS countries, the expotential smoothing method was used, which is illustrated by the graphs given in the article.Materials and methods. The theoretical basis of the research is based on the conclusions and achievements of criminology, scientific research on crime as a social phenomenon. The reasons and events influencing the dynamics of crime are taken into account. General scientific and private scientific research methods (synthesis, deduction, analysis, generalization, observation, etc.) were used in the work. The methodological basis of the study was a systematic approach, comparative and problem-based approaches.The results of the study. As a result of the analysis of the criminal situation at the transport facilities of the Russian Federation, it was found that, starting in 2020, new factors influenced the quantitative indicators of crime in transport. The introduction of restrictive measures related to the spread of coronavirus infection, their subsequent cancellation, the beginning of the geopolitical crisis in the world, provoked by the imposition of sanctions by Western countries against Russia, restrictions on international communications. closure of airports, borders, etc. they affected the directions of preventive activities of the internal affairs bodies in transport. The conclusions and forecasts made in the article determine the trends in the development of the criminal situation in transport in 2023 and allow us to determine the most important vectors of the direction of preventive activities of internal affairs bodies.Discussion and conclusion. The article identifies and substantiates the priority areas of activity of the internal affairs bodies of Russia in transport for the most effective organization of preventive activities and combating crime.

Exchange Rate Pass-Through on Prices in Nigeria—A Threshold Analysis

Persistent exchange rate depreciation and its debilitating effects on rising inflation have remained a concern in Nigeria. This article explores the effects of exchange rate pass-through on producer prices, consumer prices, export prices, import prices and the Taylor rule from 2000 to 2023, using quarterly data and adopting threshold autoregression and self-exciting smooth threshold regression methods. The findings suggest that there are non-linearities in the way that exchange rate depreciation affects prices in Nigeria. Furthermore, the findings suggest a threshold of 5 percent depreciation. Two sub-sample analyses corroborate the main findings, showing that a threshold of 5 percent is the optimum benchmark if demand and supply are not to be weakened. At this level or below, the effects of exchange rate depreciation on inflation are much lower, even though prices will rise. However, above this benchmark, the effects of depreciation on inflation are much larger, weakening consumer demand for both imported and domestic goods as well as producer supply of both exported and domestic goods and services in the economy. This result implies that an average exchange rate depreciation not higher than 5 percent within a quarter is reasonable if the Nigerian economy is to remain competitive both domestically and globally. Finally, the results suggest that the exchange rate pass-through to prices is considerably higher in Nigeria below the threshold, while it overshoots for producer prices, export prices, and import prices above the threshold. To keep inflation in check, this paper suggests that the monetary authorities should try to keep exchange rate depreciation below the established thresholds, while also considering adjusting the policy rate to take into account the exchange rate depreciation thresholds in order to keep domestic prices stable.

Comparison of Triple Exponential Smoothing and ARIMA in Predicting Cryptocurrency Prices

Cryptocurrency has emerged as a prominent digital asset over the past decade, but its high price volatility presents significant challenges for investors. This study evaluates and compares the effectiveness of the Triple Exponential Smoothing (TES) and Autoregressive Integrated Moving Average (ARIMA) methods in forecasting the prices of five major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Solana (SOL), and Ripple (XRP). TES models trends and seasonality in time series data, while ARIMA captures autoregressive patterns and moving averages. The dataset is split into 80% for training and 20% for testing, with performance evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). TES outperforms ARIMA in predicting Bitcoin and Binance Coin, achieving MAPE values of 10.38% and 13.81%, and RMSE values of 3,985.55 and 41.28, respectively. However, ARIMA shows better performance for Ethereum, Solana, and Ripple, with MAPE ranging from 8.78% to 32.84% and RMSE between 0.08 and 204.59. Notably, Ethereum has the lowest MAPE at 8.78%, while Ripple exhibits the smallest RMSE at 0.08. These findings suggest that TES is more suitable for cryptocurrencies with relatively stable price patterns, while ARIMA is better adapted to forecasting highly volatile assets. This research underscores the importance of selecting forecasting models based on the specific characteristics of each cryptocurrency

Grid Optimization of Free-Form Spatial Structures Considering the Mechanical Properties

In recent years, the application of free-form surface spatial grid structures in large public buildings has become increasingly common. The layouts of grids are important factors that affect both the mechanical performance and aesthetic appeal of such structures. To achieve a triangular grid with good mechanical performance and uniformity on free-form surfaces, this study proposes a new method called the “strain energy gradient optimization method”. The grid topology is optimized to maximize the overall stiffness, by analyzing the sensitivity of nodal coordinates to the overall strain energy. The results indicate that the overall strain energy of the optimized grid has decreased, indicating an improvement in the structural stiffness. Specifically, compared to the initial grid, the optimized grid has a 30% decrease in strain energy and a 43.3% decrease in maximum nodal displacement. To optimize the smoothness of the grid, the study further applies the Laplacian grid smoothing method. Compared to the mechanically adjusted grid, the structural mechanical performance does not significantly change after smoothing, while the geometric indicators are noticeably improved, with smoother lines and regular shapes. On the other hand, compared to the initial grid, the smoothed grid has a 21.4% decrease in strain energy and a 28.3% decrease in maximum nodal displacement.

A novel time-optimal linear toolpath smoothing method based on airthoid and circular splines for CNC machining

A novel time-optimal linear toolpath smoothing method based on airthoid and circular splines for CNC machining

The Influence of the Sports Events Industry on Sichuan's Economic Growth

The sports industry occupies an important position in China's national economy, and the sports event industry is its core industry, and its role in regional economic development has received more and more attention from all parties. This paper takes Sichuan Province as an example to discuss the role of sports events in regional development. Firstly, it is a theoretical overview. This paper explores the mechanism of its economic effect from four perspectives: economic growth, industrial structure, regional agglomeration and city brand. Second is the case study. Through the grey correlation analysis of the relationship between sports and tourism industry, using the exponential smoothing method and ARMA method, and establishing a multi-indicator model, it is concluded that sports and tourism industry have the highest correlation and play a positive role in promoting regional development.

Simulating 2D Fluid Motion with the Smooth Particle Hydrodynamic Approach

Abstract Virtual two-dimensional (2D) fluid simulation is useful for directly simulating fluids in various situations, including geological simulations for landslides and fluid simulations for teaching. This research aims to simulate the behaviour of three different types of fluids (water, coconut oil, and glycerine) in a 2D container and analyse how these three types of fluids behave under various conditions, including interactions with boundaries. The research used Python programming to simulate fluids and the Wondershare Filmora X application to combine images. The Smooth Particle Hydrodynamics method simulated fluid in a 2D container with 10,000 particles by deriving the force density field directly from the Navier-Stokes equations. The Navier-Stokes equations were utilized to find the acceleration and velocity of fluid particles by considering external forces, internal forces, and gravity through this method. Acceleration and velocity were validated due to wall collisions, collisions with boundaries, and collisions between particles, which caused changes in particle position and velocity. During visualization, the fluid velocity decreased over time due to attenuation caused by interactions between neighbour particles, particles with boundaries, and particles with walls. From the simulation, it was observed that the fluid flowed from a higher place to a lower place, with fluid particles taking the shape of the container and the surface of the fluid forming waves. On the other hand, simulations with boundaries indicated that smaller gap sizes and higher viscosities led to increased difficulty in fluid penetration into the gap within the container.

Statistical assessment of direct investment (equity capital instruments) in the economy of the Dnipropetrovsk area

Introduction. The statistics above prove that the Dnipropetrovsk area in terms of the volume of attracted foreign direct investment is confidently ranked 1st among the regions of Ukraine in 2024. The purpose of the paper is to build a model of statistical assessment of direct investment (tools of participation in capital) in the economy of Dnipropetrovsk area. Results. The shortcomings that create significant problems of formation of information and analytical support of existing models of analysis of investment cooperation between countries are identified. Adaptive forecasting based on the method of exponential smoothing. The essence of this method is that each element (level) of the time row is smoothed with a weighted average, and its weight decreases as the end of the row is removed. The forecast of direct investment (instruments of participation in capital) of EU countries in the economy of Dnipropetrovsk area by 2028 was built. Based on statistical analysis for the period 2015-2023. The forecast of direct investment (instruments of participation in capital, by types of economic activity) of EU countries in the economy of Dnipropetrovsk area by 2028. On the basis of statistical analysis for the period 2021-2023. It is emphasized that in 2021. Three industries (education (P); art, sports, entertainment and rest (R); providing other services (S)) are not made public in order to ensure compliance with the requirements of the Law of Ukraine "On Official Statistics" on the confidentiality of statistical information. Conclusion. According to the analysis, six key consequences and risks of war in Ukraine are separated when attracting foreign direct investment. As a prospect of further research in the subject area is to build a gravitational model of direct investment relationship between the economy of Dnipropetrovsk area and EU countries, for example by the following factors: GDP per capita of the country; geographical distance between Ukraine and donor countries; The population of the country. This will identify problematic issues in forming a foreign investment stimulating policy. The possibility of constructing a gravity model of foreign direct investment for Dnipropetrovsk area has been grown.