Provides functions and tools for tidy time series analysis and 
   forecasting as well as time series cross-validation. This is mainly a set 
   of wrapper and helper functions as well as some extensions for the packages 
   'tsibble', 'fable', and 'fabletools'.

Forecasting competitions are of increasing importance as a mean to learn best practices and gain knowledge. Data leakage is one of the most common issues that can often be found in competitions. Data leaks can happen when the training data contains information about the test data. For example: randomly chosen blocks of time series are concatenated to form a new time series, scale-shifts, repeating patterns in time series,  white noise is added in the original time series to form a new time series, etc.  'tsdataleaks' package can be used to detect data leakages in a collection of  time series.

A terribly-simple data base for numeric
  time series, written purely in R, so no external
  database-software is needed. Series are stored in
  plain-text files (the most-portable and enduring file
  type) in CSV format. Timestamps are encoded using R's
  native numeric representation for 'Date'/'POSIXct',
  which makes them fast to parse, but keeps them
  accessible with other software. The package provides
  tools for saving and updating series in this
  standardised format, for retrieving and joining data,
  for summarising files and directories, and for
  coercing series from and to other data types (such as
  'zoo' series).

ARIMA-model-based decomposition of quarterly and 
 monthly time series data.
 The methodology is developed and described, among others, in 
 Burman (1980) <DOI:10.2307/2982132> and 
 Hillmer and Tiao (1982) <DOI:10.2307/2287770>.

Provides deep learning models for time series forecasting 
    using Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), 
    and Gated Recurrent Unit (GRU). These models capture temporal 
    dependencies and address vanishing gradient issues in sequential data. 
    The package enables efficient forecasting for univariate time series. 
    For methodological details see Jaiswal and co-authors (2022). 
    <doi:10.1007/s00521-021-06621-3>.