Employability of Decision Support System in Data Forecasting for Internet of Things Networks

This chapter discusses the various techniques and methods related to data forecasting in Decision Support Systems. There are several methods employed for data forecasting and authors are utilizing two important aspects, namely, statistical analysis and time forecasting models. For statistical analysis, authors have employed stock market data. Further, life expectancy data from World Health Organization (WHO) are utilized for model forecasting. Each country has its own share market. For instance, India has the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). The market capitalization of all stocks worldwide is approximately US$70.75 trillion. S&P BSE POWER is one of the indices front the whole stock market index. This index signifies several companies like Tata Power, BHEL, Adani Trans, Power Grid Corp, Siemens, Adani Green Ene, etc. BSE data of power are the stock prices of exchanging among bunch of companies who consume much amount of power. Stock market helps the companies to raise their capital and to increase their investment. Also, it signals the state of economic growth of a nation. Stock market prediction helps to find the upcoming values or the future orders from selected companies. Here, in this chapter, authors are trying to explain how analysis can be done in Excel in addition to various tools, namely, R, Python, KNIME, Tableau, Power BI, and many more. Data analytics is the process of analysis or understanding datasets to draw useful conclusions. In Excel, authors will be using the Data Analysis Tab to analyze different tools. Our goal is to discover useful information leading to decision-making. The data of 193 countries have been taken here, among the many different factors, some major critical health factors are considered. It has been seen that for more than a decade the development in the health sector was intense, especially in developed nations.