Algorithm 1.

Proposed Dictionary-driven Anomaly Detection (DDAD) algorithm.

1:	procedure DDAD(Y1, …, YM, 𝒟ℳ1, …, 𝒟ℳM)
2:	Label initialization: $$l(i)=1\forall i=1,\dots K$$
3:	Separate dictionary learning problems: $$\text{Learn }[{\mathbf{D}}^1,{\mathbf{X}}^1]\text{ from }{\mathbf{Y}}_{\mathbf{l}\equiv 1}\text{ according to }𝒟{ℳ}^1\dots \text{Learn }[{\mathbf{D}}^M,{\mathbf{X}}^M]\text{ from }{\mathbf{Y}}_{\mathbf{l}\equiv 1}\text{ according to }𝒟{ℳ}^M$$
4:	Jointly consider all different types of time series: $$\mathbf{Z}=[{\mathbf{Y}}^1;\dots ;{\mathbf{Y}}^M]$$ $$\mathbf{Ẑ}=[{\mathbf{D}}^1{\mathbf{X}}^1;\dots ;{\mathbf{D}}^M{\mathbf{X}}^M]$$
5:	Compute distances in the Kernel space: $$d_i=2(C-\varphi {({\mathbf{z}}_i)}^{𝖳}\varphi ({\mathbf{ẑ}}_i))\forall i=1,\dots K$$
6:	Solve the modified OCSVM: $$\underset{\mathit{\omega },\rho }{\text{min }}\frac{{\Vert \mathit{\omega }\Vert }^2}{2}-\rho \mathrm{s}.\mathrm{t}.{\mathit{\omega }}^{𝖳}\varphi ({\mathbf{z}}_i)\ge \rho -\lambda d_i$$
7:	Update the labels: $$l(i)=\text{sgn}({\mathit{\omega }}^{𝖳}\varphi ({\mathbf{z}}_i)-\rho )\forall i=1,\dots K$$
8:	If num. max iterations not reached go to Step 3.
9:	return l     ▷ Output labels
10:	end procedure