interpTools-package: interpTools: Evaluate the statistical performance of time...

In castels/interpTools: Tools for the Systematic Testing of Interpolation Algorithms

interpTools: Evaluate the statistical performance of time series interpolators

Description

interpTools contains functions for generating artificial time series, simulating gaps, and interpolating the missing observations, with tools for subsequent analysis and visualization of the interpolators' statistical performance.

Details

Built-in interpolation algorithms:

• Nearest Neighbor (NN)

• Linear Interpolation (LI)

• Natural Cubic Spline (NCS)

• FMM Cubic Spline (FMM)

• Hermite Cubic Spline (HCS)

• Stineman Interpolation (SI)

• Kalman - ARIMA (KAF)

• Kalman - StructTS (KKSF)

• Last Observation Carried Forward (LOCF)

• Next Observation Carried Backward (NOCB)

• Simple Moving Average (SMA)

• Linear Weighted Moving Average (LWMA)

• Exponential Weighted Moving Average (EWMA)

• Replace with Mean (RMEA)

• Replace with Median (RMED)

• Replace with Mode (RMOD)

• Replace with Random (RRND)

• Hybrid Wiener Interpolator (HWI)

(or the user may pass in their own interpolating function, so long as its returned value is a single numeric vector.)

A list of built-in performance metrics can be found in the package files (~/metric_definitions.pdf)

Author(s)

• Sophie Castel (0000-0001-9086-0917)

• Wesley Burr (0000-0002-2058-1899)

References

Castel, Sophie Terra Marguerite (2020). A Framework for Testing Time Series Interpolators (Master of Science). Trent University, Peterborough, Ont. https://digitalcollections.trentu.ca/objects/etd-814

Burr, W.S. (2012). Air Pollution and Health: Time Series Tools and Analysis (PhD Thesis). Queen's University, Kingston, Ont. https://qspace.library.queensu.ca/handle/1974/7617

See Also

• tsinterp

castels/interpTools documentation built on June 7, 2024, 4:20 p.m.