Search

The superposition of independent, discrete, renewal processes produces a counting process which is also a discrete time series. The conditional distribution and correlation structure of this kind of time series may be obtained. In suitable conditions the conditional distribution has a spectrum which is exactly or approximately rational. When this is so, an ARMA can be found which matches the spectrum of the superposition.

Some inadequacies of both the traditional (exponential smoothing) and Box-Jenkins approaches to time series forecasting of economic data are investigated. An approach is suggested which integrates these two methodologies. It is based on smoothing the data using straight line segments instead of differencing to obtain stationarity, and forecasting using an autoregressive-moving-average model for the residuals from the most recent linear segment. The efficiency of this approach is calculated theoretically using a series comprising integrated white noise.

Search Results

Refine search

Refine search

Actions for selected content:

2 results

Time series formed from the superposition of discrete renewal processes

Time series modelling — an integrated approach

Search Results

Refine search

Refine search

Actions for selected content:

Save Search

2 results

Time series formed from the superposition of discrete renewal processes

Time series modelling — an integrated approach