simts - Time Series Analysis Tools

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

Last updated 1 years ago

rcpprcpparmadillosimulationtime-seriestimeseriestimeseries-dataopenblascpp

7.68 score 15 stars 4 dependents 59 scripts 277 downloads

wv - Wavelet Variance

Provides a series of tools to compute and plot quantities related to classical and robust wavelet variance for time series and regular lattices. More details can be found, for example, in Serroukh, A., Walden, A.T., & Percival, D.B. (2000) <doi:10.2307/2669537> and Guerrier, S. & Molinari, R. (2016) <arXiv:1607.05858>.

Last updated 1 years ago

signal-processingtime-serieswavelet-varianceopenblascpp

5.43 score 17 stars 1 dependents 15 scripts 141 downloads

avar - Allan Variance

Implements the allan variance and allan variance linear regression estimator for latent time series models. More details about the method can be found, for example, in Guerrier, S., Molinari, R., & Stebler, Y. (2016) <doi:10.1109/LSP.2016.2541867>.

Last updated 3 years ago

allan-varianceinertial-sensorsstatisticstime-seriescpp

4.88 score 5 stars 9 scripts 279 downloads

navigation - Analyze the Impact of Sensor Error Modelling on Navigation Performance

Implements the framework presented in Cucci, D. A., Voirol, L., Khaghani, M. and Guerrier, S. (2023) <doi:10.1109/TIM.2023.3267360> which allows to analyze the impact of sensor error modeling on the performance of integrated navigation (sensor fusion) based on inertial measurement unit (IMU), Global Positioning System (GPS), and barometer data. The framework relies on Monte Carlo simulations in which a Vanilla Extended Kalman filter is coupled with realistic and user-configurable noise generation mechanisms to recover a reference trajectory from noisy measurements. The evaluation of several statistical metrics of the solution, aggregated over hundreds of simulated realizations, provides reasonable estimates of the expected performances of the system in real-world conditions.

Last updated 2 years ago

openblascpp

4.86 score 4 stars 18 scripts 132 downloads

swag - Sparse Wrapper Algorithm

An algorithm that trains a meta-learning procedure that combines screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. This package works on top of the 'caret' package and proceeds in a forward-step manner. More specifically, it builds and tests learners starting from very few attributes until it includes a maximal number of attributes by increasing the number of attributes at each step. Hence, for each fixed number of attributes, the algorithm tests various (randomly selected) learners and picks those with the best performance in terms of training error. Throughout, the algorithm uses the information coming from the best learners at the previous step to build and test learners in the following step. In the end, it outputs a set of strong low-dimensional learners.

Last updated 1 years ago

machine-learning

3.60 score 2 stars 6 scripts 172 downloads

ib - Bias Correction via Iterative Bootstrap

An implementation of the iterative bootstrap procedure of Kuk (1995) <doi:10.1111/j.2517-6161.1995.tb02035.x> to correct the estimation bias of a fitted model object. This procedure has better bias correction properties than the bootstrap bias correction technique.

Last updated 1 years ago

3.36 score 2 stars 23 scripts 212 downloads