Table 1. Interpolation methods implemented in DATimeS.

	INTERPOLATION METHODS
MLRA	Bagging trees (BAGTREE)
	Adaptive Regression Splines (ARES)
	Boosting trees (BOOST)
	k-nearest neighbors regression (KNNR)
	Gaussian Process Regression (GPR)
	Kernel Ridge Regression (KRR)
	Locally-Weighted Polynomials (LWP)
	Support Vector Regression (SVR)
	Neural networks (NNIPL)
	Random forests (RF2)
	Boosting random trees (RF1)
	Structured Kernel Ridge Regression with linear Kernel (SKRRlin)
	Relevance Vector Machine (RVM)
	Sparse Spectrum Gaussian Process Regression (SSGPR)
	Structured Kernel Ridge Regression with RBF kernel (SKRRrbf)
	Decision trees (TREE)
	Variational Heteroscedastic Gaussian Process Regression (VHGPR)
Harmonic	Offset + Harmonic analysis
	Offset + Harmonic analysis + Linear Term
	Offset + Harmonic analysis + Linear Term
	Offset + Harmonic Analysis using Sliding Window
Conventional Methods	Linear, Polynomial, Nearest, Next, Previous, Pchip, Spline
Othters	Double Logistic curve