Provides a common interface to time series databases. The
objective is to define a standard interface so users can retrieve time
series data from various sources with a simple, common, set of
commands, and so programs can be written to be portable with respect
to the data source. The SQL implementations also provide a database
table design, so users needing to set up a time series database
have a reasonably complete way to do this easily. The interface
provides for a variety of options with respect to the representation
of time series in R. The interface, and the SQL implementations, also
handle vintages of time series data (sometime called editions or
realtime data). There is also a (not yet well tested) mechanism to
handle multilingual data documentation.
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.

Provides a common interface to time series databases. The
objective is to define a standard interface so users can retrieve time
series data from various sources with a simple, common, set of
commands, and so programs can be written to be portable with respect
to the data source. The SQL implementations also provide a database
table design, so users needing to set up a time series database
have a reasonably complete way to do this easily. The interface
provides for a variety of options with respect to the representation
of time series in R. The interface, and the SQL implementations, also
handle vintages of time series data (sometime called editions or
real-time data). There is also a (not yet well tested) mechanism to
handle multilingual data documentation.
Comprehensive examples of all the 'TS*' packages is provided in the
vignette Guide.pdf with the 'TSdata' package.
2007-2011 Bank of Canada.  2012-2017 Paul Gilbert.

Deep Learning Model for Time Series Forecasting
c(person(given = "Ronit",
family = "Jaiswal",
role = c("aut", "cre"),
email = "ronitjaiswal2912@gmail.com"),
person(given = "Girish Kumar",
family = "Jha",
role =  c("aut", "ths", "ctb")),
person(given = "Rajeev Ranjan ",
family = "Kumar",
role =  c("aut", "ctb")),
person(given = "Kapil",
family = "Choudhary",
role = c("aut", "ctb")))
RNNs are preferred for sequential data like time series, speech, text, etc. but when dealing with long range dependencies, vanishing gradient problems account for their poor performance. LSTM and GRU are effective solutions which are nothing but RNN networks with the abilities of learning both short-term and long-term dependencies. Their structural makeup enables them to remember information for a long period without any difficulty. LSTM consists of one cell state and three gates, namely, forget gate, input gate and output gate whereas GRU comprises only two gates, namely, reset gate and update gate. This package consists of three different functions for the application of RNN, LSTM and GRU to any time series data for its forecasting. For method details see Jaiswal, R. et al. (2022). <doi:10.1007/s00521-021-06621-3>.
keras, tensorflow, reticulate, tsutils, BiocGenerics, utils,
graphics, magrittr
Ronit Jaiswal [aut, cre],
Girish Kumar Jha [aut, ths, ctb],
Rajeev Ranjan Kumar [aut, ctb],
Kapil Choudhary [aut, ctb]