Titlebook: Applications of Computer Aided Time Series Modeling; Masanao Aoki,Arthur M. Havenner Conference proceedings 1997 Springer-Verlag New York,

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Youth, Consumption and Citizenshipconstruction of trading portfolios with transaction costs. Finally, bootstrapping techniques are applied to construct surrogate distributions of the out-of-sample statistics. Neural network models are found to perform more than adequately when compared with a benchmark linear model, and are able to

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Conference proceedings 1997ion 2ii the effects of sampling er- rors and model misspecification on successful modeling efforts. He argues that model misspecification is an important amplifier of the effects of sampling error that may cause symplectic matrices to have complex unit roots, a theoretical impossibility. Correct mod

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Conclusion and Future Research, and relates it to subspace methods popular in the engineering literature from the viewpoint of orthogonal projections. Except for the numerical method used to calculate orthogonal projections these two classes of methods are conceptually equivalent..

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Enabling Real-Time Business Intelligenceore accurate forecasts than a vector autoregressive model or futures market prices. Since the weight gain characteristics of holding cattle over the relevant production periods are well known, the primary uncertainty lies in the prices. Dependable price forecasts permit market participants to optimize their purchases and sales.

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Damianos Chatziantoniou,Yannis Sotiropoulostrend term, a nonstationary seasonal component, and a stationary autoregressive component. Climate scale variations in upwelling, sea surface temperature, and wind stress are examined, and regional differences in the component series on decadal scales are explored.

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On the Equivalence Between ARMA Models and Simple Recurrent Neural NetworksThis paper presents analytical results for a class of linear discrete time recurrent neural networks. The networks are shown to be able to act as autoregressive moving average models. Minimal network sizes for representing ARMA(.) models are derived, and analogies between recurrent networks and state space models are pointed out.