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Miscellaneous Time Series Filters
The mFilter package implements several time series filters useful for smoothing and extracting trend and cyclical components of a time series. The routines are commonly used in economics and finance, however they should also be interest to other areas. Currently, Christiano-Fitzgerald, Baxter-King, Hodrick-Prescott, Butterworth, and trigonometric regression filters are included in the package.
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)
Detection of Outliers in Time Series
Detection of outliers in time series following the
Chen and Liu (1993)
Enhanced Seasonal Decomposition of Time Series by Loess
Decompose a time series into seasonal, trend, and remainder components using an implementation of Seasonal Decomposition of Time Series by Loess (STL) that provides several enhancements over the STL method in the stats package. These enhancements include handling missing values, providing higher order (quadratic) loess smoothing with automated parameter choices, frequency component smoothing beyond the seasonal and trend components, and some basic plot methods for diagnostics.
Seismic Time Series Analysis Tools
Multiple interactive codes to view and analyze seismic data, via spectrum analysis, wavelet transforms, particle motion, hodograms. Includes general time-series tools, plotting, filtering, interactive display.
Locally Stationary Time Series
A set of functions that allow stationary analysis and locally stationary time series analysis.
Alternative Time Series Analysis
Contains some tools for testing, analyzing time series data and fitting popular time series models such as ARIMA, Moving Average and Holt Winters, etc. Most functions also provide nice and clear outputs like SAS does, such as identify, estimate and forecast, which are the same statements in PROC ARIMA in SAS.
Bayesian Structural Time Series
Time series regression using dynamic linear models fit using
MCMC. See Scott and Varian (2014)
Hierarchical and Grouped Time Series
Provides methods for analysing and forecasting hierarchical and
grouped time series. The available forecast methods include bottom-up,
top-down, optimal combination reconciliation (Hyndman et al. 2011)
Time Series Modeling for Air Pollution and Health
Tools for specifying time series regression models.