Statistical Forecasting Research Articles

INTEGRATION OF THE UKRAINIAN ECONOMY INTO THE EURO-ATLANTIC COMMUNITY IN THE CONTEXT OF INTERNATIONALIZATION AND GLOBALIZATION PROCESSES: CONCEPT AND ASSESSMENT

The purpose of the article is to present the author's point of view on the conceptual apparatus that clarifies the definition of such economic categories as "internationalization", "economic integration", "inclusiveness", "globalization", etc. As a result, the essence of the processes of internationalization and integration of Ukraine into the democratized economic system of Euro-Atlantic countries is revealed. The object of the study is to analyze the complex processes of internationalization, globalization and integration of the Ukrainian economy into the competitive environment of Euro-Atlantic countries. The research methodology is based on general scientific and fundamental provisions of economic theory, international economics and statistics. The conceptual basis of the article is the world economic theories on the assessment of globalization processes in the development of the world economy. Processing and analysis of statistical data, modeling and forecasting of the identified trends were carried out using modern software. The most important theoretical and practical results that characterize the scientific novelty of the study and the personal contribution of the author are: identification of significant relationships and interdependencies between the processes of internationalization and international integration; meaningful analysis of the prerequisites for the emergence and deepening of the process of economic integration and the intensity of the influence of centripetal forces; the concept of inclusive growth, which is considered from the perspective of coherence of development goals of countries that form a globally integrated Euro-Atlantic community. Based on the main results of the study, the authors make several important conclusions and recommendations, including the following. Taxonomy of categories "internationalization", "economic integration", "inclusiveness", "globalization". Based on the analysis of the characteristic features of unions (international economic and full integration), it is proved that the latter require research, analysis and, as a result, the introduction of a common monetary, fiscal, credit policy, etc. One of the essential characteristics of the ability of any country to "fit" into the international system is the compliance of the development goals of such a country with the economic behavior of its government with international standards.

VOLATILITY FORECASTING AFTER REMOVING THE NOISE: DOES IT REALLY PAY OFF?

The analysis in this paper represents the first attempt to examine whether the forecasting performance of one of the GARCH models can be improved after removing the noise from the stock market returns. Doing the analysis on eight developed stock markets helps in providing reliable results that can be generalized to more stock markets. This paper then examines the ability of GARCH models to forecast stock return volatility under a range of statistical forecasts. We specifically employ the multiscale-based approach, namely the maximum overlap discrete wavelet transformation, to remove the noisy data from the return series. Based on related theoretical literature, we assume that noise trading makes the forecasting performance more misleading, especially at the short-term horizon. Our particular interest then is whether wavelet de-noising of the data before estimation affects the ability of the models to provide accurate forecasts at a short investment horizon. To de-noise the data, we use soft thresholding and Stein’s Unbiased Risk Estimator to obtain the decomposition level-based threshold limit. Our key results demonstrate that de-noising returns improves the accuracy of volatility forecasts regardless of whether we used statistical metrics or tests of equal predictive accuracy. Moreover, in terms of a particular volatility model, the asymmetric GARCH approach tends to be preferred although this result is not universal. Indeed, the central result from our analysis is that the process of de-noising is more important than the specific model.

Statistical and Grey Forecasting of the Inbound Tourism to Malawi

Tourism is one of the fastest growing and complex industries in Malawi. This article is aimed at forecasting the future pattern of the tourism industry in Malawi from the year 2018 to 2028. The study has employed grey forecasting model EGM (1, 1, α, θ) to predict the future pattern of tourism based on the initial data sets sourced from the data base of the World Data over Malawi. The findings of this study showed that the grey forecasting model EGM (1, 1, α, θ) performed well in forecasting the future pattern of tourism by comparing with the linear regression and exponential regression. The forecast has revealed that the tourism industry will grow with an average of 20.84% by the year 2028 based on the current conditions. Following the present findings, the tourism industry should therefore continue improving the current conditions in order to attract more tourists. Furthermore, the industry should continue to supply tourism products and services that can satisfy the increasing demand of the international travel experiences as well as the future growing number such as constructing more standard hotels, proper transportation and communication.

A Basic Time Series Forecasting Course with Python

The aim of this paper is to present a set of Python-based tools to develop forecasts using time series data sets. The material is based on a 4-week course that the author has taught for 7 years to students on operations research, management science, analytics, and statistics 1-year MSc programmes. However, it can easily be adapted to various other audiences, including executive management or some undergraduate programmes. No particular knowledge of Python is required to use this material. Nevertheless, we assume a good level of familiarity with standard statistical forecasting methods such as exponential smoothing, autoregressive integrated moving average (ARIMA), and regression-based techniques, which is required to deliver such a course. Access to relevant data, codes, and lecture notes, which serve as based for this material, is made available (see https://github.com/abzemkoho/forecasting) for anyone interested in teaching such a course or developing some familiarity with the mathematical background of relevant methods and tools.

Know Before You Go: Data-Driven Beach Water Quality Forecasting.

Forecasting environmental hazards is critical in preventing or building resilience to their impacts on human communities and ecosystems. Environmental data science is an emerging field that can be harnessed for forecasting, yet more work is needed to develop methodologies that can leverage increasingly large and complex data sets for decision support. Here, we design a data-driven framework that can, for the first time, forecast bacterial standard exceedances at marine beaches with 3 days lead time. Using historical data sets collected at two California sites, we train nearly 400 forecast models using statistical and machine learning techniques and test forecasts against predictions from both a naive "persistence" model and a baseline nowcast model. Overall, forecast models are found to have similar sensitivities and specificities to the persistence model, but significantly higher areas under the ROC curve (a metric distinguishing a model's ability to effectively parse classes across decision thresholds), suggesting that forecasts can provide enhanced information beyond past observations alone. Forecast model performance at all lead times was similar to that of nowcast models. Together, results suggest that integrating the forecasting framework developed in this study into beach management programs can enable better public notification and aid in proactive pollution and health risk management.

Análisis estadístico y modelo de pronóstico SARIMA aplicado al consumo de energía eléctrica en instalaciones universitarias

Analyzing the energy consumption behavior in buildings is essential for implementing energy-saving and efficient energy use measures without losing attention to the comfort inside the buildings. In this study, a statistical analysis and time series forecast of the energy situation of a group of buildings in a university academic unit in Mexico City was conducted. Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used for the forecast with electrical energy consumption data from 55 months. Training and test partitions were created with these data to generate two SARIMA models. The results showed a strong dependence on the school cycle of electricity consumption, in addition to a shift in the cycle in the first year of the study. The mean absolute percentage error (MAPE) for the training partitions created shows that the best fit is provided by the SARIMA (3,1,1) (1,0,0)12 model for the 48-month separation. In comparison, the SARIMA (2,1,2) (1,0,0)12 model does so for the 43-month test partition. The confidence intervals for the 7- and 12-month forecast are less wide for the SARIMA (3,1,1) (1,0,0)12 model than for the SARIMA (2,1,2) (1,0,0)12 model. Statistical analysis and time series modeling allows a better understanding of the building stock's energy performance and strengthens the energy audit to design or implement energy saving or efficient energy use measures.

The National Hurricane Center Tropical Cyclone Model Guidance Suite

Abstract The National Hurricane Center (NHC) uses a variety of guidance models for its operational tropical cyclone track, intensity, and wind structure forecasts, and as baselines for the evaluation of forecast skill. A set of the simpler models, collectively known as the NHC guidance suite, is maintained by NHC. The models comprising the guidance suite are briefly described and evaluated, with details provided for those that have not been documented previously. Decay-SHIFOR is a modified version of the Statistical Hurricane Intensity Forecast (SHIFOR) model that includes decay over land; this modification improves the SHIFOR forecasts through about 96 h. T-CLIPER, a climatology and persistence model that predicts track and intensity using a trajectory approach, has error characteristics similar to those of CLIPER and D-SHIFOR but can be run to any forecast length. The Trajectory and Beta model (TAB), another trajectory track model, applies a gridpoint spatial filter to smooth winds from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model. TAB model errors were 10%–15% lower than those of the Beta and Advection model (BAM), the model it replaced in 2017. Optimizing TAB’s vertical weights shows that the lower troposphere’s environmental flow provides a better match to observed tropical cyclone motion than does the upper troposphere’s, and that the optimal steering layer is shallower for higher-latitude and weaker tropical cyclones. The advantages and disadvantages of the D-SHIFOR, T-CLIPER, and TAB models relative to their earlier counterparts are discussed. Significance Statement This paper provides a comprehensive summary and evaluation of a set of simpler forecast models used as guidance for NHC’s operational tropical cyclone forecasts, and as baselines for the evaluation of forecast skill; these include newer techniques that extend forecasts to 7 days and beyond.

Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting

Despite major improvements in weather and climate modelling and substantial increases in remotely sensed observations, drought prediction remains a major challenge. After a review of the existing methods, we discuss major research gaps and opportunities to improve drought prediction. We argue that current approaches are top-down, assuming that the process(es) and/or driver(s) are known—i.e. starting with a model and then imposing it on the observed events (reality). With the help of an experiment, we show that there are opportunities to develop bottom-up drought prediction models—i.e. starting from the reality (here, observed events) and searching for model(s) and driver(s) that work. Recent advances in artificial intelligence and machine learning provide significant opportunities for developing bottom-up drought forecasting models. Regardless of the type of drought forecasting model (e.g. machine learning, dynamical simulations, analogue based), we need to shift our attention to robustness of theories and outputs rather than event-based verification. A shift in our focus towards quantifying the stability of uncertainty in drought prediction models, rather than the goodness of fit or reproducing the past, could be the first step towards this goal. Finally, we highlight the advantages of hybrid dynamical and statistical models for improving current drought prediction models. This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.

Use of contextual and model-based information in adjusting promotional forecasts

Despite improvements in statistical forecasting, human judgment remains fundamental to business forecasting and demand planning. Typically, forecasters do not rely solely on statistical forecasts; they also adjust forecasts according to their knowledge, experience, and information that is not available to statistical models. However, we have limited understanding of the adjustment mechanisms employed, particularly how people use additional information (e.g., special events and promotions, weather, holidays) and under which conditions this is beneficial. Using a multi-method approach, we first analyse a UK retailer case study exploring its operations and the forecasting process. The case study provides a contextual setting for the laboratory experiments that simulate a typical supply chain forecasting process. In the experimental study, we provide past sales, statistical forecasts (using baseline and promotional models) and qualitative information about past and future promotional periods. We include contextual information, with and without predictive value, that allows us to investigate whether forecasters can filter such information correctly. We find that when adjusting, forecasters tend to focus on model-based anchors, such as the last promotional uplift and the current statistical forecast, ignoring past baseline promotional values and additional information about previous promotions. The impact of contextual statements for the forecasting period depends on the type of statistical predictions provided: when a promotional forecasting model is presented, people tend to misinterpret the provided information and over-adjust, harming accuracy.

Stochastic tropical cyclone precipitation field generation

AbstractTropical cyclones are important drivers of coastal flooding which have severe negative public safety and economic consequences. Due to the rare occurrence of such events, high spatial and temporal resolution historical storm precipitation data are limited in availability. This article introduces a statistical tropical cyclone space‐time precipitation generator given limited information from storm track datasets. Given a handful of predictor variables that are common in either historical or simulated storm track ensembles such as pressure deficit at the storm's center, radius of maximal winds, storm center and direction, and distance to coast, the proposed stochastic model generates space‐time fields of quantitative precipitation over the study domain. Statistically novel aspects include that the model is developed in Lagrangian coordinates with respect to the dynamic storm center that uses ideas from low‐rank representations along with circular process models. The model is trained on a set of tropical cyclone data from an advanced weather forecasting model over the Gulf of Mexico and southern United States, and is validated by cross‐validation. Results show the model appropriately captures spatial asymmetry of cyclone precipitation patterns, total precipitation as well as the local distribution of precipitation at a set of case study locations along the coast. We additionally compare our model against a widely‐used statistical forecast, and illustrate that our approach better captures uncertainty, as well as storm characteristics such as asymmetry.

Short-Term Weather Forecast Skill of Artificial Neural Networks

Abstract We evaluate the short-term weather forecast performance of three flavors of artificial neural networks (NNs): feed forward back propagation, radial basis function, and generalized regression. To prepare the application of the NNs to an operational setting, we tune NN hyperparameters using over two years of historical data. Five objective guidance products serve as predictors to the NNs: North American Mesoscale and Global Forecast System model output statistics (MOS) forecasts, the High-Resolution Rapid Refresh (HRRR) model, National Weather Service forecasts, and the National Blend of Models product. We independently test NN performance using 96 real-time forecasts of temperature, wind, and precipitation across 11 U.S. cities made during the WxChallenge, a weather forecasting competition. We demonstrate that all NNs significantly improve short-range weather forecasts relative to the traditional objective guidance aids used to train the networks. For example, 1-day maximum and minimum temperature forecast error is 20%–30% lower than MOS. However, NN improvement over multiple linear regression for short-term forecasts is not significant. We suggest this may be attributed to the small number of training samples, the operational nature of the experiment, and the short forecast lead times. Regardless, our results are consistent with previous work suggesting that applying NNs to model forecasts can have a positive impact on operational forecast skill and will become valuable tools when integrated into the forecast enterprise. Significance Statement We used approximately two years of historical weather data and objective forecasts for a number of cities to tune a series of artificial neural networks (NNs) to forecast 1-day values of maximum and minimum temperature, maximum sustained wind speed, and quantitative precipitation. We compare forecast error against common objective guidance and multiple linear regression. We found that the NNs exhibit about 25% lower error than common objective guidance for temperature forecasting and 50% lower error for wind speed. Our results suggest that NNs will be a valuable contributor to improving weather forecast skill when adopted into the existing forecast enterprise.

A Transformer-Based Regression Scheme for Forecasting Significant Wave Heights in Oceans

In this article, we present a novel approach for forecasting significant wave heights in oceanic waters. We propose an algorithm based on the WaveWatch III, differencing, and a transformer neural network (Transformer). The data becomes stationary after first-order differencing, performed with the observed significant wave height and the wave height forecasts obtained from WaveWatch III. We perform a case study on a group of 92 buoys using WaveWatch III hindcasts. The Transformer model then provides the statistical forecasts of the residuals. The Transformer-based proposed framework obtains the root mean square error of 0.231 m for two days ahead forecasting. Our proposed method outperforms existing state-of-the-art machine learning and numerical approaches for significant wave heights prediction. Our results suggest that combining numerical and machine learning approaches gives better performance than using either alone.

A size-of-loss model for the negatively skewed insurance claims data: applications, risk analysis using different methods and statistical forecasting

The future values of the expected claims are very important for the insurance companies for avoiding the big losses under uncertainty which may be produced from future claims. In this paper, we define a new size-of-loss distribution for the negatively skewed insurance claims data. Four key risk indicators are defined and analyzed under four estimation methods: maximum likelihood, ordinary least squares, weighted least squares, and Anderson Darling. The insurance claims data are modeled using many competitive models and comprehensive comparison is performed under nine statistical tests. The autoregressive model is proposed to analyze the insurance claims data and estimate the future values of the expected claims. The value-at-risk estimation and the peaks-over random threshold mean-of-order-p methodology are considered.

The technology of intelligent analysis in predicting the epidemiological situation of hiv infection in the west kazakhstan region

The article uses the main directions of development of mathematical modeling of control and spread of human immunodeficiency virus (HIV infection), dynamic and structural classification of the epidemic process. Statistical data on the West Kazakhstan region for the period 2010-2020 were taken from the regional center for the prevention and control of AIDS of the health department of the Akimat of the West Kazakhstan region, with a description of the spread of the virus, depending on the available data and tasks in the world and in the Republic of Kazakhstan, a retrospective, cluster analysis of HIV-infected people was carried out. The characteristics of the morbidity schedule are given, the risk is identified, statistical forecasts of morbidity are checked. In scientific and practical studies, the prevalence of criteria and observations of the epidemic was considered from the information technology side. Examples are given using a forecasting system and existing intelligent mathematical model systems. One of the important directions is the prediction of the consequences of the HIV epidemic, the study of immunological dynamics within one organism, the problem of predicting the development of the epidemic situation at the population level. Data Mining classification methods were used to process HIV-infected people and analyze the situation by age characteristics. The prognosis of the morbidity of the West Kazakhstan region by the age group of HIV-infected people over a 10-year period was carried out using the Statistica application software package.

A Deterministic–Statistical Hybrid Forecast Model: The Future of the COVID-19 Contagious Process in Several Regions of Mexico

More than two years after the declaration of the COVID-19 pandemic, we are still experiencing contagious waves. As this is a long-lasting process, it becomes relevant to have a predictive tool to identify the intensively active places within a region. This study presents the development of a forecasting model applied to foresee the progress of the contagious process in Mexico and its regions. The method comprehends aspects of deterministic and probabilistic modeling. The deterministic part comprises the classical SIR model with some adjustments. The probabilistic part builds and populates a three-dimensional array, which is then used to describe and recall the probabilities of going from one status to another after some time, very much like a Markovian process. The process status is modeled as the combination of two conditions: the infection exponential growth parameter and a proxy variable we named “permissiveness” that accounts for all combined social activity factors affecting COVID-19 propagation. The results offer projections of the exponential growth parameter and the number of newly infected individuals for three weeks into the future. The proposed method’s capabilities allow for predicting newly COVID-19-infected individuals with reasonable precision while capturing the characteristic dynamics and behavior of the modeled system.

Establishing Opportunities and Limitations of Forecast Use in the Operational Management of Highly Constrained Multiobjective Water Systems

The potential utility of forecast information to improve reservoir control is well established, but physical, operational, and institutional constraints inhibit the full realization of these benefits. To help integrate forecast technology into operational water management, it is necessary to establish the opportunities and limitations of forecast use within the current system management framework, so that water managers and planners can identify in practice what constraints or aspects of control policy design most limit forecast utility. In this work, we explore the value of forecasts in the current outflow management plan of the Lake Ontario-St. Lawrence River basin, which is the first regulation policy of this system to use water supply forecasts to guide release decisions. We assess the value of forecasts for many-objective system management (e.g., upstream and downstream flood risk reduction, hydropower production, commercial navigation costs, recreational boating benefits, and wetland restoration), as a function of forecast accuracy, timing, and interactive effects, with system constraints and control rule structures. Results show that a 20%–30% reduction in forecast error over current statistical forecasts leads to observable improvements in most objectives, which grow significantly with additional forecast accuracy. Realizing these benefits is possible by increasing forecast accuracy, particularly in the springtime when tradeoffs between objectives are the greatest. However, we also identify one objective (wetland restoration) that declines with improved forecast skill, as well as operational constraints that limit forecast utility under the current outflow management plan. Overall, these results suggest that key aspects of the current control policy could be revised to better utilize forecast information, especially as forecasts improve over time.

Impact of 3D printing on car shipping supply chain logistics in the Middle East

Three-dimensional printing (3DP) will lead to future changes in the structure and supply chain operation of businesses. While prevailing studies focus on qualitative assessments, this study quantitatively estimates 3DP impact on the maritime shipping volume and fleet type by (i) estimating the shipping volume reduction due to the mainstreaming of 3DP, and (ii) identifying the shipping fleet types that might be impacted by the change in the supply chain. The supply chain of the automotive industry in the Middle Eastern (ME) region was considered, and different statistical forecasting tools and linear programming models were applied to predict the demand for vehicles and quantify shipping volume reduction. Using a range of 3D printers (1–10), a 26–39% reduction in ton-miles of shipping by 2040 was predicted. Additionally, a 29–45% possible change in the volume of shipping from car carrier (roll-on/roll-off) fleets to bulk cargo fleet types was also predicted. This study also identified potential 3DP plant locations in the ME region ascertaining the potential loss and benefit that can be gained by the maritime shipping industry. It helps shipping manufacturers, operators, and owners consider future demand in terms of the quantity and types of fleets.

Statistical model and forecasting of bandwidth requirements on aggregating nodes of FTTX network using Monte Carlo computations for different demographic segments

Telecom service providers are on a relentless task of development and upgradation of their networks to support increasing requirements of internet users. FTTX landline connections serves multiple users connected on a single line hence need more robust and high data rate capabilities. In FTTX network, link capacity allocation for a service node to aggregating node requires statistical study of usage patterns of customers of the service areas. Different service areas have different usage patterns hence their statistical distributions are different. In this article we have acquired a yearlong data of peak hour utilizations from customers of the largest FTTX service provider in Pakistan to develop a statistical model. We developed an empirical distribution of peak hours of the customers mapped on day clock. This distribution has been used in Monte Carlo computation to find maximum data rate requirements on aggregation nodes in comparison to subscribed data rates of all users on a service node. Further a forecasting model has been used to predict the growth in subscriber demands in different demographic segments for coming years. A combination of maximum possible data rate requirement at aggregation node and forecasted subscriber data rates led to develop a forecast of data rate requirement for next five years in different demographic segments.

Feature-Driven Economic Improvement for Network-Constrained Unit Commitment: A Closed-Loop Predict-and-Optimize Framework

As an important application in the power system operation and electricity market clearing, the network-constrained unit commitment (NCUC) problem is usually executed by Independent System Operators (ISO) in an open-looped predict-then-optimize (O-PO) process, in which an upstream prediction (e.g., on renewable energy sources (RES) and loads) and a downstream NCUC are executed in a queue. However, in the O-PO framework, a statistically more accurate prediction may not necessarily lead to a higher NCUC economics against actual RES and load realizations. To this end, this paper presents a closed-loop predict-and-optimize (C-PO) framework for improving the NCUC economics. Specifically, the C-PO leverages structures (i.e., constraints and objective) of the NCUC model and relevant feature data to train a cost-oriented RES prediction model, in which the prediction quality is evaluated via the induced NCUC cost instead of the statistical forecast errors. Therefore, the loop between the prediction and the optimization is closed to deliver a cost-oriented RES power prediction for NCUC optimization. Lagrangian relaxation is adopted to accelerate the training process, making the C-PO applicable for real-world systems. Case studies on an IEEE RTS 24-bus system and an ISO-scale 5655-bus system with real-world data show that the proposed C-PO can effectively improve the NCUC economics as compared to the traditional O-PO.

Modelling and forecasting Nifty 50 using hybrid ARIMA-GARCH Model

This study proposes an estimation technique for developing the best Öt ARIMA- GARCH model to predict the closing values of Nifty 50. The study put forward di§erent methods to resolve the issue of non-stationarity in mean as well as variance of the series before starting the estimation process. This study has applied autoregressive integrated moving- average (ARIMA), generalized autoregressive conditional heteroscedasticity (GARCH), ex- ponential GARCH (EGARCH) and threshold GARCH (TGARCH) model along with other estimation procedures on the daily closing prices of Nifty 50 from Jan 1, 2009 to Dec 30, 2019. Finally, the study identiÖes ARIMA(2,1,2)-EGARCH(1,1,1) as best model to predict the closing prices of Nifty 50. The Öndings indicate that the static forecast provides better results as compared to the dynamic forecast. These research Öndings will add to the tool kit of domestic as well as international portfolio managers and investors to frame suitable NIFTY trade strategies with least possible risks.