The reconciliation of systems of time series subject to both temporal and contemporaneous constraints can be solved in such a way that the temporal profiles of the original series be preserved “at the best” (movement preservation principle). Thanks to the sparsity of the linear system to be solved, a feasible procedure can be developed to solve simultaneously the problem. A two-step strategy might be more suitable in the case of large systems: firstly, each series is aligned to the corresponding temporal constraints according to a movement preservation principle; secondly, all series are reconciled within each low-frequency period according to the given constraints. This work compares the results of simultaneous and two-step approaches for medium/large datasets from real-life and discusses conditions under which the two-step procedure can be a valid alternative to the simultaneous one.

Simultaneous and Two-step Reconciliation of Systems of Time Series.

Di Fonzo, Tommaso
2009

Abstract

The reconciliation of systems of time series subject to both temporal and contemporaneous constraints can be solved in such a way that the temporal profiles of the original series be preserved “at the best” (movement preservation principle). Thanks to the sparsity of the linear system to be solved, a feasible procedure can be developed to solve simultaneously the problem. A two-step strategy might be more suitable in the case of large systems: firstly, each series is aligned to the corresponding temporal constraints according to a movement preservation principle; secondly, all series are reconciled within each low-frequency period according to the given constraints. This work compares the results of simultaneous and two-step approaches for medium/large datasets from real-life and discusses conditions under which the two-step procedure can be a valid alternative to the simultaneous one.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3442445
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