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An Efficient Time Series Subsequence Pattern Mining and Prediction Framework with an Application to Respiratory Motion Prediction

AAAI 2016 · Time series › Causal and generative time series · approach: State-space and latent dynamics forecasting · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace orthogonal-polynomial approximation with DFT representation (ablation)OPVNN · Respiratory motion, 27 patients, horizon 10R2 prediction accuracyHurt
replace R2-weighted ensemble with simple average of best neighbors (ablation)OPVNN · Respiratory motion, 27 patients, horizon 10R2 prediction accuracyHurt
remove time-delay matching-error penalty (equal weights over the pattern) (ablation)OPVNN · Respiratory motion, 27 patients, horizon 10R2 prediction accuracyHurt
remove adaptive alignment in pattern searching (use raw patterns) (ablation)OPVNN · Respiratory motion, 27 patients, horizon 10R2 prediction accuracyHurt
use OPVNN instead of res-TVSAR, wLMS, SVRpred and SARIMArespiratory motion prediction · Respiratory motion, 27 patients, horizons up to 30 stepsR2 prediction accuracyHelped

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