Temporal aggregation, systematic sampling, and the Hodrick–Prescott filter

Temporal aggregation, systematic sampling, and the Hodrick-Prescott filter

Maravall and del Río (2001), analized the time aggregation properties of the Hodrick-Prescott (HP) filter, which decomposes a time series into trend and cycle, for the case of annual, quarterly, and monthly data, and showed that aggregation of the disaggregate component cannot be obtained as the exact result from direct application of an HP filter to the aggregate series. The present paper show...

The Effects of Systematic Sampling and Temporal Aggregation on Discrete Time Long Memory Processes and Their Finite Sample Properties

This study investigates the effects of varying sampling intervals on the long memory characteristics of certain stochastic processes. We find that although different sampling intervals do not affect the decay rate of discrete time long memory autocorrelation functions in large lags, the autocorrelation functions in short lags are affected significantly. The level of the autocorrelation function...

Spatial Online Sampling and Aggregation

The massive adoption of smart phones and other mobile devices has generated humongous amount of spatial and spatio-temporal data. The importance of spatial analytics and aggregation is everincreasing. An important challenge is to support interactive exploration over such data. However, spatial analytics and aggregation using all data points that satisfy a query condition is expensive, especiall...

The Compressed-sampling Filter (csf)

The common approaches to sample a signal generally follow the well-known Nyquist-Shannon’s theorem: the sampling rate must be at least twice the maximum frequency presented in the signal. A new emerging field, compressed sampling (CS), has made a paradigmatic step to sample a signal with much less measurements than those required by the Nyquist-Shannon’s theorem when the unknown signal is spars...

Temporal Aggregation