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Two Functions for Generalized SARIMA Time Series Simulation
Write SARIMA models in (finite) AR representation and simulate
generalized multiplicative seasonal autoregressive moving average (time) series
with Normal / Gaussian, Poisson or negative binomial distribution.
The methodology of this method is described in Briet OJT, Amerasinghe PH, and
Vounatsou P (2013)
Methods for Handling and Analyzing Time Series of Satellite Images
Provides functions and methods for: splitting large raster objects
into smaller chunks, transferring images from a binary format into raster
layers, transferring raster layers into an 'RData' file, calculating the
maximum gap (amount of consecutive missing values) of a numeric vector,
and fitting harmonic regression models to periodic time series. The homoscedastic
harmonic regression model is based on G. Roerink, M. Menenti and W. Verhoef (2000)
Archaeological Time Series
A toolkit for archaeological time series and time intervals.
This package provides a system of classes and methods to represent and
work with archaeological time series and time intervals. Dates are
represented as "rata die" and can be converted to (virtually) any
calendar defined by Reingold and Dershowitz (2018)
Time Series Regression
Time series regression. The dyn class interfaces ts, irts(), zoo() and zooreg() time series classes to lm(), glm(), loess(), quantreg::rq(), MASS::rlm(), MCMCpack::MCMCregress(), quantreg::rq(), randomForest::randomForest() and other regression functions allowing those functions to be used with time series including specifications that may contain lags, diffs and missing values.
Time Series Analysis
Analyzes autocorrelation and partial autocorrelation using surrogate methods and bootstrapping, and computes the acceleration constants for the vectorized moving block bootstrap provided by this package. It generates percentile, bias-corrected, and accelerated intervals and estimates partial autocorrelations using Durbin-Levinson. This package calculates the autocorrelation power spectrum, computes cross-correlations between two time series, computes bandwidth for any time series, and performs autocorrelation frequency analysis. It also calculates the periodicity of a time series.
'data.table' Time-Series
High-frequency time-series support via 'nanotime' and 'data.table'.
Time Series Plot
A fast and elegant time series visualization package. In addition to the standard R plot types, this package supports candle sticks, open-high-low-close, and volume plots. Useful for visualizing any time series data, e.g., stock prices and technical indicators.
Time Series Representations
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 various normalisation methods (min-max, z-score, Box-Cox, Yeo-Johnson), and forecasting accuracy measures are implemented.
Time Series Performance
A tool to calculate the performance of a time series in a specific date or period. It is more intended for data analysis in the fields of finance, banking, telecommunications or operational marketing.
Time Series Disaggregation
Disaggregates low frequency time series data to higher frequency series. Implements the following methods for temporal disaggregation: Boot, Feibes and Lisman (1967)