Temporal disaggregation methods are used to disaggregate and interpolate a low frequency time series to a higher frequency series. This can be done without additional information or by exploiting the information contained in one or more indicators. All disaggregation methods ensure that either the sum, the average, the first or the last value of the resulting high frequency series is consistent with the low frequency series.
Implements cointegration/cotrending rank selection algorithm in Guo and Shintani(2011). Paper: "Consistant Cotrending rank selection when both stochastic and nonlinear deterministic trends are present", Preprint, Feb 2011.
This is an substitute for the %V and %u formats which are not implemented on Windows. In addition, the package offers functions to convert from standard calender format yyyy-mm-dd to and from ISO 8601 week format yyyy-Www-d.
Estimation and simulation of CARMA(p,q), continuous-time-autoregressive-moving-average models
This package is designed for time series data. Fits Vector Autoregressive models and Vector Autoregressive models with Exogenous Inputs. For speedup, fastVAR can use multiple cpu cores to calculate the estimates. For very large systems, fastVAR uses Lasso penalty to return very sparse coefficient matrices. Regression diagnostics can be used to compare models, and prediction functions can be used to calculate the n-step ahead prediction. Faster implementations in C coming soon.
A collection of estimation, forecasting and diagnostic tools for autoregressive fractionally integrated moving-average process (ARFIMA).
This package provides various Singular Spectrum Analysis routines.
Classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and simple trajectories. Utility functions are provided for plotting data as map sequences (lattice or animation) or multiple time series. Methods for spatial and temporal selection and subsetting, as well as for retrieving coordinates, print, summary, etc.
S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. In particular, this package is highly oriented to hydrological modelling tasks. The focus of this package has been put in providing a collection of tools useful for the daily work of hydrologists (although an effort was made to optimise each function as much as possible, functionality has had priority over speed). Bugs / comments / questions / collaboration of any kind are very welcomed, and in particular, datasets that can be included in this package for academic purposes.
S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.