NVDModel: Class NVDModel

In realerikrani/nvdr: NVD Vulnerability Data Analyser

Description

Manages forecasts of one CWE.

Direct Subclasses

• FitModel,

• FcastModel

Private attributes

• cwe CWE ID (default NA)

• start_year the first year of a time period (default NA)

• end_year the last year of a time period (default NA)

• train the training set (default NA)

• test the test set (default NA)

• fcast the forecasts as a "forecast" object (default NA)

• assessment the MAE, RMSE, MAPE and MASE in numeric form (default NA)

• fcast_period the number of months necessary to forecast (default NA)

• residuals_not_random the logical value indicating whether the models' residuals are not uncorrelated; cannot be used in the cases of ARFIMAModel, BaggedETSModel, StructTSModel and NNARModel for which ACF plots must be used instead (default F)

• residuals_not_normal the logical value indicating whether the models' residuals are not normally distributed (default F)

• bootstrap_not_used the logical value indicating whether the forecast intervals were not generated from bootstrapped residuals (default T)

• pi_ignored the logical value; when F, then the forecast intervals can be taken seriously, otherwise the model's residuals must be checked (default F)

• significance_level the significance level used in the Shapiro-Wilk, Ljung-Box and Breusch-Godfrey hypothesis cheking (default 0.05)

• measures = c("MAE", "RMSE", "MAPE", "MASE") a character vector attribute used in the source code

• tset_char = "Test set" a character attribute used in the source code (default F)

Public methods

• new(cwe, cwe_ts, start_year, end_year, end_month) creates new NVD object

• getStartYear() returns private attribute start_year

• getEndYear() returns private attribute end_year

• getTrainingSet() returns private attribute train

• getTestSet() returns private attribute test

• getFcasted() returns private attribute fcast

• getMethod() returns attribute method of private attribute fcast

• getAssessment() returns private attribute assessment

• getFcastPeriod() returns private attribute fcast_period

• getTSetChar() returns private attribute tset_char

• getResiduals(model) returns residuals from model processed by zoo::na.approx; model is obtained from FitModel's method getFitted or NVDModel's method getFcasted

• getMergedTrainTestSet() returns a stats::ts object after merging both the training set and the test set

• getSignificanceLevel() returns private attribute significance_level

• areResidualsNotRandom() returns private attribute residuals_not_random

• areResidualsNotNormal() returns private attribute residuals_not_normal

• isPiIgnored() returns private attribute pi_ignored

• isBootstrapNotUsed() returns private attribute bootstrap_not_used

• setSignificanceLevel(significance) sets significance as the value of private attribute significance_level

• setResidualsNotRandom(testresult) sets testresult as the value of private attribute residuals_not_random

• setResidualsNotNormal(testresult) sets testresult as the value of private attribute residuals_not_normal

• setBootstrapNotUsed(logical_value) sets logical_value as the value of private attribute bootstrap_not_used

• setPiIgnored(logical_value) sets logical_value as the value of private attribute pi_ignored

• setFcasted(fcast_result) sets fcast_result as the value of private attribute fcast

• setFcastPeriod(period) sets period as the value of private attribute fcast_period

• assessModel() calculates and sets MAE, RMSE, MAPE, MASE values as the value of private attribute assessment

• testResidualsRandomnessBox(residuals, df) uses residuals and degrees of freedom arguments and performs the Ljung-Box test; returns whether the obtained p-value is less or equal than the significance level

• testResidualsRandomnessBreusch(residuals, df, fitted_model) uses residuals, degrees of freedom and fitted model arguments and performs the Breusch-Godfrey test; returns whether the obtained p-value is less or equal than the significance level

• testResidualsNormality(residuals) uses residuals arguments and performs the Shapiro-Wilk test; returns whether the obtained p-value is less or equal than the significance level

• getPlot() returns a plot of forecast results and actual values as a red line when the forecasting results are available in the private attribute fcast; adds an exclamation mark to y-axis label when self$areResidualsNotRandom() | (self$areResidualsNotNormal() & self$isBootstrapNotUsed()) | self$isPiIgnored()

• compareCombinedAccuracy(fcast1, fcast2) takes two collections of forecasts and returns a logical value indicating whether the first collection of forecasted values has bigger amount of smaller MAE, RMSE, MAPE and MASE values than the other collection of forecasted values

• findLambda(training_set) returns the lambda found by forecast::BoxCox.lambda based on the training set

Note

This class's private method 'residuals_lag' is using GPL-3 licensed code from (R package "forecast" by Rob Hyndman, Mitchell O'Hara-Wild, Christoph Bergmeir, Slava Razbash and Earo Wang) https://github.com/robjhyndman/forecast/blob/c87f33/R/checkresiduals.R from lines 122 and 123 to calculate the lag parameter.

realerikrani/nvdr documentation built on May 29, 2019, 8:06 a.m.