Beschreibung
Scientific Study from the year 2015 in the subject Mathematics - Stochastics, language: English, abstract: The work describes two new heuristic approaches to time series analysis and forecasting for business purposes. Both approaches avoid any assumptions according to assumed process attributes behind the data (stochastic process, stationarity, normal distribution of random noise). Those methods engineer data of any kind of business processes. Only unidentified (inherent) process structures are used for forecasting. Speed represents the drive of current business development. IT is about allowing automatic self-synchronizing (production) processes. Big Data potentially offers the identification of hidden structures. In many companies mobile information access is being used. Multi-Channel B2C, B2B and M2M are gaining the managerial pole position. But nevertheless the quality of data is the key for producing excellent results. It is important that planning is based on as realistic data as possible. After roughly more than 35 years Business Forecasting is back in the focus.
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