TSdbi Interface to PADI Time Series Server (for e.g. Fame)
Provides methods for generics in the TSdbi package to
connect through a protocol for application database interface (PADI)
to a time series database (e.g. Fame).

TSdbi methods for PADI Time Series Server Interface

Time Series Database Interface extensions for PADI Time Series
Server
Provides methods for generics in the TSdbi package to
connect through a protocol for application database interface
(PADI) to a time series database (e.g. Fame).

1995-2011 Bank of Canada. 2012 Paul Gilbert.

TSdbi extensions for PADI Time Series Server
Provides methods for generics in the TSdbi package to
connect through a protocol for application database interface
(PADI) to a time series database (e.g. Fame).  Comprehensive
examples of all the TS* packages is provided in the vignette
Guide.pdf with the TSdata package.

Create Interactive Plots on Time Series Dataset
c(person("Yongkai", "Qiu",
role = c("aut", "cre"),email = "yongkai@umich.edu"),
person("Zhe", "Yin", role = "aut"),
person("Ivo","Dinov",role = "aut"),
person("SOCR","team",role = "aut"))
To better visualize time-series dataset,
'TSplotly' package provides an effective mechanism to utilize
the powerful 'plotly' package for graphing
time series data. It contains 5 core functions:
TSplot(): create interactive plot on time series data or fitted
ARIMA(X) models.
ADDline(): add lines on existing 'TSplot()' objects, as needed.
GGtoPY(): create a convenient way to transform (reformat) 'ggplot2'
datasets into a format that can work on 'plot_ly()'.
GTSplot(): create multiple 'plot_ly()' lines (time-series) based on
data frames containing multiple time-series data.
TSplot_gen(): a more general version of function 'TSplot()' that
can work on any time format.
forecast, plotly, zoo, ggplot2, dcemriS4, prettydoc
Yongkai Qiu [aut, cre],
Zhe Yin [aut],
Ivo Dinov [aut],
SOCR team [aut]

person("Peter", "Laurinec", email = "tsreprpackage@gmail.com",
role = c("aut", "cre"))
Methods for representations (i.e. dimensionality reduction, preprocessing, feature extraction) of time series to help more accurate and effective time series data mining.
Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also min-max and z-score normalisations, and forecasting accuracy measures are implemented.
Rcpp (>= 0.12.12), MASS, quantreg, wavelets, mgcv, dtt
knitr, rmarkdown, ggplot2, data.table, moments