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In this research investigation, the author presents a detailed Simple Novel Extrapolation Based Open and Closed System Ensemble Forecasting Scheme. Also, an RCB Universal Representation Theory based One Step Forecasting Model is presented. Furthermore, a holistic Causality Dimensions based Ensemble Forecasting correction to the forecasts is presented. In this schema, firstly, the authors create all possible pairs of two points each from the given Series to predict, and find each pair’s projection using the straight line joining the two points of the pair. These forecasts are then Ensembled using a scheme based on the Inner Product between the RCB Type Normalized Full Series (only Dependent Variable Co-ordinates) augmented with its One Step Forecast with each of the thusly form Type RCB Normalized Sub-Series (the two pints augmented with their linear Forecast). An alternative schema of Ensembling using the RCB Universal Represenattion Theoretic True Life Aspect Primality Norm and True Redundancy Aspect Primality Norm are used in place of the Similarity (formerly stated Inner Product) and Dissimilarity. This Similarity or Inner Product Value is used as the Weight for Ensembling. That is, a recursive equation, gotten in terms of the One Step Closed System Forecast is constructed, from which we actually compute the One Step Closed System Forecast. Then, a Causal One Step Closed System Forecast is computed using author’s notions of the same detailed in the literature and stated in the references. The authors then compute the One Step Open System Forecast using a Recursive Exhaustive Pyramidal Open System Analysis Scheme proposed in authors previous works on the rigour of the RCB Universal Representation Theories. The authors also propose synthetic production of possible gaps in an experimentally derived series using authors previously proposed theories on RCB Special Curves analysis and computation of the same.

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Universal Forecasting Schemes – III {Theoretical Model for a Simple Novel Linear Extrapolation Based Closed and Open System Ensemble Forecasting Scheme}

  • 会议论文

  • 发表日期：

    2025年

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  • Ramesh Chandra Bagadi,
  • Hussain Bahia

摘要

In this research investigation, the author presents a detailed Simple Novel Extrapolation Based Open and Closed System Ensemble Forecasting Scheme. Also, an RCB Universal Representation Theory based One Step Forecasting Model is presented. Furthermore, a holistic Causality Dimensions based Ensemble Forecasting correction to the forecasts is presented. In this schema, firstly, the authors create all possible pairs of two points each from the given Series to predict, and find each pair’s projection using the straight line joining the two points of the pair. These forecasts are then Ensembled using a scheme based on the Inner Product between the RCB Type Normalized Full Series (only Dependent Variable Co-ordinates) augmented with its One Step Forecast with each of the thusly form Type RCB Normalized Sub-Series (the two pints augmented with their linear Forecast). An alternative schema of Ensembling using the RCB Universal Represenattion Theoretic True Life Aspect Primality Norm and True Redundancy Aspect Primality Norm are used in place of the Similarity (formerly stated Inner Product) and Dissimilarity. This Similarity or Inner Product Value is used as the Weight for Ensembling. That is, a recursive equation, gotten in terms of the One Step Closed System Forecast is constructed, from which we actually compute the One Step Closed System Forecast. Then, a Causal One Step Closed System Forecast is computed using author’s notions of the same detailed in the literature and stated in the references. The authors then compute the One Step Open System Forecast using a Recursive Exhaustive Pyramidal Open System Analysis Scheme proposed in authors previous works on the rigour of the RCB Universal Representation Theories. The authors also propose synthetic production of possible gaps in an experimentally derived series using authors previously proposed theories on RCB Special Curves analysis and computation of the same.