Research map / Time series / Causal and generative time series
An Efficient Time Series Subsequence Pattern Mining and Prediction Framework with an Application to Respiratory Motion Prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace orthogonal-polynomial approximation with DFT representation (ablation) | OPVNN · Respiratory motion, 27 patients, horizon 10 | R2 prediction accuracy | Hurt |
| replace R2-weighted ensemble with simple average of best neighbors (ablation) | OPVNN · Respiratory motion, 27 patients, horizon 10 | R2 prediction accuracy | Hurt |
| remove time-delay matching-error penalty (equal weights over the pattern) (ablation) | OPVNN · Respiratory motion, 27 patients, horizon 10 | R2 prediction accuracy | Hurt |
| remove adaptive alignment in pattern searching (use raw patterns) (ablation) | OPVNN · Respiratory motion, 27 patients, horizon 10 | R2 prediction accuracy | Hurt |
| use OPVNN instead of res-TVSAR, wLMS, SVRpred and SARIMA | respiratory motion prediction · Respiratory motion, 27 patients, horizons up to 30 steps | R2 prediction accuracy | Helped |
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