LOWESS, EMA and Polynominal Trend additions#3
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- Add buildEmaTrend, buildPolynomialTrend, buildLowessTrend to series.ts with getEmaAlpha and getLowessBandwidth helpers - Port all three methods to anomaly_detection.py (_build_ema, _build_polynomial_trend, _build_lowess) - Fix LOWESS bug: bandwidth was accepted but ignored; now used as the neighbourhood cutoff and distance normaliser in both TS and Python - Refactor history-analysis.worker.ts to import math from @/cards/history/analysis instead of duplicating it - Widen trend_method union in history-series.ts and NormalizedAnalysis to include ema, polynomial_trend, lowess - Add UI options for all three methods in analysis-trend-group; show window selector for rolling_average, ema, lowess; hide for linear_trend and polynomial_trend - Add chart render options for the three new methods with distinct line styles - Add static fixture files (trend-dataset.json, trend-expected.json) and cross-language spec covering golden master regression and TS/Python consistency - Add README section documenting all trend methods with when-to-use guidance - Add scripts/gen-trend-expected.py for regenerating golden fixtures - version bump from 0.4.2 -> 0.5.0
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bandwidthparameter was accepted but never used — distances were normalised by the furthest point instead, making all window sizes produce identical curveshistory-analysis.worker.tsto import math from@/cards/history/analysisinstead of duplicating the implementationsanomaly_detection.pyfor backend trend-residual analysis