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Advanced Forecasting Schemes
Ramesh Bagadi
其他書名
Original Research Work of Mr. Ramesh Chandra Bagadi
出版Independently Published, 2019-10-10
主題Education / Research
ISBN16990349749781699034972
URLhttp://books.google.com.hk/books?id=sl1nywEACAAJ&hl=&source=gbs_api
註釋In this research investigation, keeping in view the limitations of Time Series Analysis, a new and novel Forecasting Scheme For Time Series Sequence is Developed by the author. For establishing this scheme, firstly a Novel concept Of Higher Order Sequences Of Primes is introduced by the author. This novel Scheme exploits the spacing between the elements of the Higher Order Sequences Of Primes and standard existing notion of Correlations Analysis (as used in the Time Series Analysis scheme), with minor modifications to them, for forecasting the term of the considered Time Series Sequence. The major axiomatical assumption made by the author in developing this method of Forecasting Analysis is that Natural Systems that Evolve along Time, Evolve in a fashion as dictated by the Evolution of Sequences Of Primes and Sequences Of Higher Order Primes or the linear (or non-linear*) functions of them. Non-Linear functions of the same are not necessary as long as we consider Linear Functions of the same to very high length of the aforementioned Sequences. Finally, the given Sequence along with its Forecasted Value is Reversed and we use author's Forecasting Model to predict the last value of this Reversed Sequence (i.e., which is the First Value of the given Sequence), by deliberately omitting it for prediction purposes. This gives us the Error, if any. In Forecasting Science, the most popular method currently in use is Time Series Analysis. The application of Time Series Analysis method is a very lengthy and circuitous and is also not always infallible. To make amends to this end, the author has developed a totally new and novel Forecasting Scheme based on the principles of interspacing of the elements of the Sequence of Prime Numbers and Higher Order Sequences Of Prime Numbers that are also coined by the author. Forecast Error Minimization is also achieved by using an author's method of Sequence (along with its Forecasted Value) Reversion.