How Shapelet-Based Patterns Predict Financial Market Direction

The article presents a two‑stage framework—SIMPC for invariant multivariate pattern clustering and JISC‑Net for shape‑subclass detection—that achieves accurate and interpretable financial market direction forecasts, outperforming strong baselines on Bitcoin and S&P 500 datasets across most metric‑dataset combinations.

DTWDirection PredictionInterpretability

0 likes · 11 min read

How Shapelet-Based Patterns Predict Financial Market Direction

JD Tech Talk

Sep 28, 2020 · Big Data

Trajectory Similarity Measures: Euclidean, DTW, LCSS, EDR, Hausdorff, Fréchet, OWD, and LIP

The article introduces various trajectory similarity metrics—including Euclidean distance, Dynamic Time Warping, Longest Common Sub‑Sequence, Edit Distance on Real sequences, Hausdorff distance, Fréchet distance, One‑Way Distance, and Locality In‑between Polylines—explaining their definitions, advantages, limitations, and visual illustrations for spatio‑temporal data analysis.

DTWEDRFréchet

0 likes · 11 min read

Trajectory Similarity Measures: Euclidean, DTW, LCSS, EDR, Hausdorff, Fréchet, OWD, and LIP