object RollingValidation
The RollingValidation object provides 'k'-fold rolling validations, e.g.,
for 'm = 1200' and 'k = 10', 'kt = 20':
1: tr(ain) 0 until 800, te(st) 800 until 840 2: tr(ain) 40 until 840, te(st) 840 until 880 3: tr(ain) 80 until 880, te(st) 880 until 920 4: tr(ain) 120 until 920, te(st) 920 until 960 5: tr(ain) 160 until 960, te(st) 960 until 1000 6: tr(ain) 200 until 1000, te(st) 1000 until 1040 7: tr(ain) 240 until 1040, te(st) 1040 until 1080 8: tr(ain) 280 until 1080, te(st) 1080 until 1120 9: tr(ain) 320 until 1120, te(st) 1120 until 1160 10: tr(ain) 360 until 1160, te(st) 1160 until 1200
In rolling validation for this case, each training dataset has 800 instances, while each testing dataset has 40. Re-training occurs before every 'kt = 20' forecasts are made (2 re-trainings per testing dataset for this case).
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- def crossValidate(model: PredictorMat with ForecasterMat, k: Int = 5, kt_: Int = 10, h: Int = 1): Array[Statistic]
- def crossValidate2(model: ForecasterVec, k: Int = 5, kt_: Int = 10, h: Int = 1): Array[Statistic]
- def crossValidate3(model: PredictorMat2 with ForecasterMat, k: Int = 5, kt_: Int = 10, h: Int = 1): Array[Statistic]
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- def trSize(m: Int): Int
Calculate the size (number of instances) for a training dataset.
Calculate the size (number of instances) for a training dataset.
- m
the size of the full dataset
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