A State Space Framework for Automatic Forecasting Using Exponential Smoothing Methods.

We provide a new approach to automatic business forecasting based on an extended range of exponential smoothing methods. Each method in our taxonomy of exponential smoothing methods can be shown to be equivalent to the forecasts obtained from a state space model. This allows (1) the easy calculation of the likelihood, the AIC and other model selection criteria; (2) the computation of prediction intervals for each method; and (3) random simulation from the underlying state space model. We demonstrate the methods by applying them to the data from the M-competition on the M3-competition.Automatic forecasting, exponential smoothing, prediction intervals, state space models.

Software Evolution through Dynamic Adaptation of Its OO Design

Abstract. In this paper we present a proposal for safely evolving a software system against run-time changes. This proposal is based on a reflective architecture which provides objects with the ability of dynamically changing their behavior by using their design information. The meta-level system of the proposed architecture supervises the evolution of the software system to be adapted that runs as the base-level system of the reflective architecture. The meta-level system is composed of cooperating components; these components carry out the evolution against sudden and unexpected environmental changes on a reification of the design information (e.g., object models, scenarios and statecharts) of the system to be adapted. The evolution takes place in two steps: first a meta-object, called evolutionary meta-object, plans a possible evolution against the detected event then another meta-object, called consistency checker meta-object validates the feasibility of the proposed plan before really evolving the system. Meta-objects use the system design information to govern the evolution of the base-level system. Moreover, we show our architecture at work on a case study