Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.372202
Title: Generalised exponentially weighted regression and dynamic Bayesian forecasting models
Author: Akram, Muhammad
ISNI:       0000 0001 3403 942X
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 1984
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Abstract:
In this dissertation a new Forecasting Methodology, called Generalised Exponentially Weighted Regression (G.E.W.R) is presented. This Methodology is based on Linear Filtering using an Exponentially Weighted System and a Bayesian formulation developed. It is particularly designed to analyse discrete time series driven by Autoregressive and Moving Average type Coloured Noise processes. In order to elaborate the theory various theorems and corollaries are given. For the implementation of G.E.W.R. various parsimonious Bayesian Dynamic Linear Models and Normal Discount Models for Low and High Frequency Components of time series with or without Seasonality and Cyclicity are introduced. For theoretical and computational purposes recurrence relations for the Precision and Transformation Matrices are developed. For the unknown variance case an automatic (self-tuning) on line Bayesian Learning Procedure is introduced. For Complex Systems a procedure to construct the State Space Models is given and, for practitioners, methods of reparameterising Dynamic Linear Models is given. In order to demonstrate the performance of G.E.W.R. the theory is applied to various simulated data sets and real life economic and industrial time series. In all cases the Methodology not only generates one-step ahead optimum forecasts in a Minimum Mean Square Error (M.M.S.E.) sense but also provides reasonable long term forecasts.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.372202  DOI: Not available
Keywords: HA Statistics ; QA Mathematics
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