Acronym: WDTW Type: Whole Series Year: 2011 Publication: Pattern Recognition

 Description: Jeong $et\:al.$ describe WDTW, which adds a multiplicative weight penalty based on the warping distance between points in the warping path. It favours reduced warping, and is a smooth alternative to the cutoff point approach of using a warping window. When creating the distance matrix $M$, a weight penalty $w_{|i-j|}$ for a warping distance of $|i-j|$ is applied, so that $$M_{i,j}= w_{|i-j|} (a_i-b_j)^2.$$ A logistic weight function is used, so that a warping of $a$ places imposes a weighting of $$w(a)=\frac{w_{max}}{1+e^{-g\cdot(a-m/2)}},$$ where $w_{max}$ is an upper bound on the weight (set to 1), $m$ is the series length and $g$ is a parameter that controls the penalty level for large warpings. The larger $g$ is, the greater the penalty for warping. Source Code: Weighted Dynamic Time Warping Code
 Published Results: Recreated Results:

 Published Dataset: Result: Adiac 0.3640 Beef 0.6000 CBF 0.0020 Coffee 0.1330 FaceAll 0.2570 FaceFour 0.1360 FiftyWords 0.1940 Fish 0.1260 GunPoint 0.0400 Lightning2 0.1000 Lightning7 0.2000 OliveOil 0.1880 OSULeaf 0.3720 SwedishLeaf 0.1380 SyntheticControl 0.0020 Wafer 0.0020 Yoga 0.1650

 This algorithm doesn't have any recreated results.