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The first step is to forecast the number of projects (frequency) for the chosen time span.
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Fig. 9 ACF (left) and PACF (right) of project frequency.
Table 5 summarize the performance of the discussed univariate time series modeling for forecasting project frequency.
Figure 9 (right) shows the PACF correlogram of the project frequency.
However, the cumulative datasets provide the project frequency for different timeframes.
Figure 9 (left) shows the ACF correlogram of the project frequency.
Figure 6 plots the project frequency for each month during the 12 years period (2003 2015).
In order to better understand the characteristics of project frequency series and capture them, a set of preliminary analysis was undertaken.
Based on the results of autocorrelation and partial auto correlation an ARMA (p = 8, q = 8) is the right choice to model the project frequency series.
The multicaptive method is not appropriate for series with negative or zero values and as the project frequency series has zero values in some months, only the additive method is implemented.
Table 4 Summary of ADF test on project frequency series Lag t-statistic P-value ADF with intercept and trend 11 −3.15 0.100 ADF with intercept 11 −3.12 0.027 ADF without intercept and trend 12 −0.43 0.52.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com