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Thailand is currently grappling with a severe dengue fever outbreak, with a rising threat to public health as the rainy season and El Niño draw near. This year has witnessed a troubling surge in dengue cases, prompting the Ministry of Public Health (MoPH) to issue warnings that the numbers may hit a three-year peak. Dengue outbreaks in Thailand have historically followed a cyclical pattern, excluding COVID-19 years. This research employs data analysis and predictive modeling to forecast the forthcoming dengue case numbers in Thailand, facilitating better public health preparedness. It also incorporates data visualization for enhanced data exploration. Various forecasting models, including Exponential Smoothing, Polynomial Fitting and Random Forest, are deployed to predict dengue cases within the constraints of our data. This study offers valuable insights into the potential trajectory of dengue cases in Thailand, aiding proactive measures to combat the outbreak.

This leading primary study is about modeling multifractal wavelet scale time series data using multiple wavelet coherence (MWC), continuous wavelet transform (CWT) and multifractal detrended fluctuation analysis (MFDFA) and forecasting with vector autoregressive fractionally integrated moving average (VARFIMA) model. The data is acquired from Yahoo Finances!, which is composed of 1671 daily stock market of eastern (NIKKEI, TAIEX, KOPSI) and western (SP500, FTSE, DAX) markets. Once the co-movement dependencies on time-frequency space are determined with MWC, the coherent data is extracted out of raw data at a certain scale by using CWT. The multifractal behavior of the extracted series is verified by MFDFA and its local Hurst exponents have been calculated obtaining root mean square of residuals at each scale. This inter-calculated fluctuation function time series has been re-scaled and used to estimate the process with VARFIMA model and forecasted accordingly. The results have shown that the direction of price change is determined without difficulty and the efficiency of forecasting has been substantially increased using highly correlated multifractal wavelet scale time series data.

The problem of determining the European-style option price in incomplete markets is examined within the framework of stochastic optimization. An analytic method based on the stochastic optimization is developed that gives the general formalism for determining the option price and the optimal trading strategy (optimal feedback control) that reduces the total risk inherent in writing the option. The cases involving transaction costs, the stochastic volatility with uncertainty, stochastic adaptive process, and forecasting process are considered. A software package for the option pricing for incomplete markets is developed and the results of numerical simulations are presented.

Ensemble mean forecast errors during a tropical cyclone event are probed with a spherical wavelet transform constructed by the lifting scheme. Coefficient spectra and associated filtered error components are examined during the forecast, with an emphasis on feature detection, for mean sea level pressure and wind components. Leading wavelet coefficients within a reference circle centered on the estimated cyclone track demonstrate a clear affinity for local error extrema, reflecting the transform’s feature detection capacity. Compression performance of the transform is also demonstrated by truncated wavelet expansions, which exhibit contrasting behavior reflecting fundamental structural differences between the wind and pressure error fields.

In this paper, we analyse the diffusion mechanism of wind power over the last two decades in the leading countries, namely China, the United States, Germany, India and Spain. For each country, three prominent models of technology diffusion (Logistic, Bass and Gompertz) were fitted and the best model is identified based on AIC, BIC and adjusted R² criteria. The selected diffusion model in each case is then characterised with respect to the policy mechanisms. Often, research follows the "one size fits all" approach and tends to propose one model to define diffusion for all. Here we find that it is not necessarily true. The study then proposes the causal relationship between parameters of the selected model and corresponding policies along with the socioeconomic structure for a country to corroborate our findings. Further, forecasts were generated to predict the saturation point of the diffusion path and solutions are proposed to expand the diffusion curve.

Atmospheric and oceanic climate factors and conditions play a crucial role in modulating seasonal/annual tropical cyclone activity in the North Atlantic Ocean Basin. In the following, correlations between North Atlantic tropical cyclone activity including frequency of occurrence and pathways are explored, with special emphasis on hurricanes. The value of two-dimensional and three-dimensional data sets representing climate patterns is investigated. Finally, the diagnostic study of historical tropical cyclone and hurricane temporal and spatial variability and relationships to climate factors lead to a statistical prognostic forecast, made in April, 2010, of the 2010 tropical cyclone and hurricane season. This forecast is tested both retrospectively and presently and is shown to be quite accurate. Knowing the probability of the frequency of occurrence, i.e. the numbers of named storms to form in general and the number of hurricanes (NHs) that are likely to form, is important for many societal sectors. However, the reliable forecasts of probable pathways of predicted events, specifically the likely NH land falls along the coastlines of the United States, should have great potential value to emergency planners, the insurance industry, and the public. The forecast provided in this study makes such a prognostication. As the 2010 hurricane season has progressed, an update of the goodness of the forecast is shown to be quite accurate in numbers of named events, hurricanes, major hurricanes (MHs), and landfalls. The mathematical and statistical methodology used in this study, which could be coupled to next generation "empirical modal decomposition," suggests that this may signal a new era in the future of tropical cyclone forecasting, including the reliable prognostication of numbers of events, intensities of events, and the pathways of those events. The ability to reliably predict the probability and location of land falls of these destructive events would be very powerful indeed.

Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 2007–2022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)-based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price.

Utilizing a mixed data sampling (MIDAS) approach, we show that a daily newspaper-based index of uncertainty associated with infectious diseases can be used to predict, both in- and out-of-samples, low-frequency movements of output growth for the United States (US). The predictability of monthly industrial production growth and quarterly real Gross Domestic Product (GDP) growth during the current period of heightened economic uncertainty due to the COVID-19 pandemic is likely to be of tremendous value to policymakers.

As a developing economy, three major economic problems witnessed in the Gambia are the growing unemployment rate, migration (immigration and rural–urban drift) leading to urban population growth and the growing semi-skilled working population in the face of unemployment. This study seeks to answer the question of how the Gambian economy can plan to overcome these problems, coupled with post-COVID-19 global economic shocks, through a technically planned capacity development. In this paper, the trends in variables representing capacity development indicators, migration, unemployment and working population in the Gambia are studied using the Autoregressive Integrated Moving Average (ARIMA) model. To project a system of interrelationship among these variables in the Gambia, the study employs the Vector Autoregressive (VAR) forecast analysis for the period between 1990 and 2019, thereafter generates a five-year forecast. The findings confirm that investment into the educational sector in developing economies is bound to yield increasing return to scale in the next five years. Investment into education, training and skill acquisition, if done, will attract the transfer of technical and managerial skills and technology for the purpose of building up general national capacity in such a developing economy.

The aim of this paper is to propose a new methodology for hydroelectric energy forecasting. A new approach for selection of the number of eigenvalues in SSA is also proposed. In this paper it is proposed the hierarchical clustering associated to PCA and integrated to ARIMA models. The proposed approach is applied to forecast the affluent flow in a hydroelectric plant located at Parana River Basin, Brazil. As a matter of fact, modeling such series is quite important for the optimal dispatch of the energy generation in Brazil due to the heavy participation of hydro plants in the country (over 85% of the generated energy comes from hydro plants).

This paper seeks to provide an alternative forecast to that provided by the Energy Information Administration (EIA) on energy-related monthly CO₂ emissions in the United States. The data on CO₂ emissions from petroleum, natural gas, coal and total fossil fuels obtained via the EIA covering the period January 2005 to November 2013 is analysed and then forecasted using ARIMA, Holt-Winters, and Exponential Smoothing prior to introducing the Singular Spectrum Analysis (SSA) technique for CO₂ emissions forecasting. A new combination forecast (EIA-SSA) is also introduced by merging the SSA and EIA forecasts, and is seen outperforming all models including the EIA forecast. Finally, the EIA-SSA model is used to provide an alternative 12 month ahead outlook for US energy-related CO₂ emissions from December 2013 to November 2014. This research is expected to influence the methodology adopted by the EIA for forecasting CO₂ emissions in the future by improving the accuracy of the forecasts, and the impact of this study will be clearer upon comparing the actual CO₂ emissions in US with the EIA, and EIA-SSA forecasts over the 12 month period which follows.

A new approach that jointly uses Singular Spectrum Analysis, Bootstrap and the automatic procedures ets and auto.arima, called SSA.Boot, were successfully presented in previous works. Then, this works applies the SSA.Boot procedure to a new set of series to validate the method. The results founded, consistent with the approach presentation paper, shows that there is gain by incorporating synthetical series before forecasting. For all the six tested series the errors measures were small in the case where the SSA.Boot were used confirming that this is a promising methodology.

In this paper, a new approach is proposed to improve forecasting performances. We analyze the co-movement of precious metals (daily data of gold, silver and platinum starting from July, 2011) using multiple wavelet coherence and determine the movement dependencies on frequency–time space. The data is split into frequencies using scale by scale continuous wavelet transform. All three time series retaining the same frequency scale are (i) selected, (ii) inversed and (ii) forecasted using multivariate model, Vector Auto Regressive Moving Average (VARMA). We conclude that the efficiency of VARMA forecasting is substantially increased because of same frequency highly correlated time series obtained by using scale by scale wavelet transform. Moreover, the direction of price shift (increasing/decreasing trend) is prospected to an adequately distinguishable degree.

In this paper, dynamic four-dimensional (4D) correlation of eastern and western markets is analyzed. A wavelet-based scale-by-scale analysis method has been introduced to model and forecast stock market data for strongly correlated time intervals. The daily data of stock markets of SP500, FTSE and DAX (western markets) and NIKKEI, TAIEX and KOSPI (eastern markets) are obtained from 2009 to the end of 2016 and their co-movement dependencies on time–frequency space using 4D multiple wavelet coherence (MWC) are determined. Once the data is detached into levels of different frequencies using scale-by-scale continuous wavelet transform, all of the time series possessing the same frequency scale are selected, inversed and forecasted using multivariate model, vector autoregressive moving average (VARMA). It is concluded that the efficiency of forecasting is increased substantially using the same-frequency highly correlated time series obtained by scale-by-scale wavelet transform. Moreover, the increasing or decreasing trend of prospected price shift is foreseen fairly well.

Nowadays, there is no data analysis scheme about the container aquaculture. In this paper, considering the data analysis demands, feedforward neural network (FFNN) based on back-propagation algorithm is built. The problems of classic BP neural network elements analysis models and optimization method is put forward. BP neural network aquaculture elements analysis optimized by Elicitation Johnson reduction algorithm using distinguishable matrix satisfy the experiment evaluation.

During the development of socio-economic systems with a certain periodicity, there arise crises initiated by the impact of political, financial, economic, epidemiological, and other nature events. Over the past few years, the global community faced the spreading and destabilizing effects of COVID-19 on all areas of life, business, production, commercial and non-commercial structures, and public administration. The chapter attempts to reveal and interpret current trends in the development of socio-economic and transport and logistics systems within COVID-19, revealing the reasons that characterize the emergence of imbalances in economic and transport and logistics systems. Within the framework of the studied topic, the chapter substantiates the necessity of forming forecast values for the sustainable functioning of supply chains, considering the possibility of interpreting the forecast in several variations. The basis for the formation of forecast values of development in the future is the fact that the value and volume of online commerce are already increasing every year. The unprecedented surge in online sales associated directly with the COVID-19 pandemic has brought volumes closer to the futures indicators of 2025. According to expert opinion, electronic sales have every chance to absorb 80% of the market by 2030. The research used classical methods of scientific knowledge, including system analysis, synthesis, graphical interpretation of the given data, comparative analysis, and abstraction. The summarizing part of the research substantiates the author’s opinion on the formation of areas of sustainable operation of logistics companies in the medium term. It is necessary to invest in the development of IT technologies and transport and storage infrastructure of logistic complexes and form optimal paths of transport processes. Logistics companies that will adhere to these recommendations should make clear scripted forecasts in several development options, considering the emergence of crisis and force majeure situations. One of the conclusions of the research is the fact that competition within the framework of global logistics systems, which has expanded to all levels of their organization up to the local one, determines the main success parameters in today’s business, not only the quality and value of the product but, to a greater extent, the time parameters of logistics services, the rapid development of last-mile logistics, and the optimization of intermodal and multimodal operations.

This research paper originally build up the Wavelet — Kalman Filtering Hybrid Estimating and Forecasting Algorithm (WKHEFA), which incorporates the advantages of both Kalman filtering and wavelet analysis. And it successfully forecasts the 30-minute trading volume of Shanghai Stock Exchange based on WKHEFA.

Traffic accidents have become a more and more important factor to restrict the development of economy and affect the safety of human. Gray System quests for the inner relation through the original data, this is an approach to find out the rule of data through other data. Highway traffic accident forecasting model based on Gray System uses some original data, through theory of Gray System, processing the data and modeling GM (1,1). Through the validation of actual data, error of GM (1,1) is minor, it can be used in actual forecasting.

The following sections are included:

• INTRODUCTION

• TESTS OF THE SIGNIFICANCE OF α AND β
  • Hypothesis Testing and Confidence Interval for β and α
  • Two-Tailed z Test Versus Two-Tailed t Test for β
  • Two-Tailed t Test for α
  • One-Tailed t Test for β
  • Confidence interval for β
  • The F Test Versus the t Test
  • Procedure for Using the F Test
  • The Relationship Between the F Test and the t Test

• TEST OF THE SIGNIFICANCE OF ρ
  • t Test for Testing ρ = 0
  • z Test for Testing ρ = 0 or ρ = Constant

• CONFIDENCE INTERVAL FOR THE MEAN RESPONSE AND PREDICTION INTERVAL FOR THE INDIVIDUAL RESPONSE
  • Point Estimates of the Mean Response and the Individual Response
  • Interval Estimates of forecasts
  • Calculating Standard Errors
  • Confidence Interval for the Mean Response and Prediction Interval for the Individual Response
  • Using MINITAB to Calculate Confidence Interval and Prediction Interval

• BUSINESS APPLICATIONS
  • The Relationship Between Layoff Rate and the Unemployment Compensation Subsidy Rate
  • Market Model Estimation and Analysis
  • Relationship Between Audited and Book Inventory Value
  • Hamburger Sales: Predicting Profits⁵
  • The Impact of Cable TV Penetration on Network Share TV Revenues

• USING COMPUTER PROGRAMS TO DO SIMPLE REGRESSION ANALYSIS

• Summary

• Apiendix 14A Impact of Measurement Error and Proxy Error on Slope Estimates

• Appendix 14B The Relationship Between the F Test and the t Test

• Appendix 14C Derivation of Variance for Alternative Forecasts

• Questions and Problems

In order to provide data reference in protecting Beihai Silver Beach this article used the moving average method spearman rank correlation analysis method and R/S rescaled range method to analyze the typhoon tendency in Beihai Silver Beach and predicted the future tendency from the typhoon frequency intensity and extremes. The results show that: In the past 60 years there is a downward trend in the typhoon frequency in Beihai Silver Beach with a growing trend in the intensity and extremes though those three trends aren't very obvious; the typhoon frequency in Beihai Silver Beach will rise in the future so do the intensity and extremes. For ensuring Silver Beach is not affected by the change of typhoon in the future we must make some targeted and reasonable responses.