# Rob Hyndman

#### 34 packages on CRAN

#### 5 packages on GitHub

Functions for demographic analysis including lifetable calculations; Lee-Carter modelling; functional data analysis of mortality rates, fertility rates, net migration numbers; and stochastic population forecasting.

Data sets from the book "Forecasting with exponential smoothing: the state space approach" by Hyndman, Koehler, Ord and Snyder (Springer, 2008).

All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998) <https://robjhyndman.com/forecasting/>.

Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

All data sets required for the examples and exercises in the book "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos. All packages required to run the examples are also loaded.

All data sets required for the examples and exercises in the book "Forecasting: principles and practice" (2nd ed, 2018) by Rob J Hyndman and George Athanasopoulos <https://otexts.com/fpp2/>. All packages required to run the examples are also loaded.

All data sets required for the examples and exercises in the book "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos <https://OTexts.com/fpp3/>. All packages required to run the examples are also loaded.

Computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate,and multimodal regression.

The R package *fable* provides methods and tools for displaying and analysing time series forecasts. Data, model and forecast objects are all stored in a tidy format.

The 1001 time series from the M-competition (Makridakis et al. 1982) <DOI:10.1002/for.3980010202> and the 3003 time series from the IJF-M3 competition (Makridakis and Hibon, 2000) <DOI:10.1016/S0169-2070(00)00057-1>.

This package includes a set of tools for implementing the Monash Electricity Forecasting Model for electricity demand. The package requires the following data as input: half-hourly/hourly electricity demands; half-hourly/hourly temperatures at one or two locations; seasonal demographical and economical data; public holiday data. The formats of the required data are described in the help files.

Methods and tools for generating forecasts at different temporal frequencies using a hierarchical time series approach.

Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.

Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

BFAST integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting and characterizing abrupt changes within the trend and seasonal components. BFAST can be used to analyze different types of satellite image time series and can be applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics. The algorithm can be extended to label detected changes with information on the parameters of the fitted piecewise linear models. BFAST monitoring functionality is added based on a paper that has been submitted to Remote Sensing of Environment. BFAST monitor provides functionality to detect disturbance in near real-time based on BFAST-type models. BFAST approach is flexible approach that handles missing data without interpolation. Furthermore now different models can be used to fit the time series data and detect structural changes (breaks).

A collection of 'LaTeX' styles using 'Beamer' customization for pdf-based presentation slides in 'RMarkdown'. At present it contains 'RMarkdown' adaptations of the LaTeX themes 'Metropolis' (formerly 'mtheme') theme by Matthias Vogelgesang and others (now included in 'TeXLive'), the 'IQSS' by Ista Zahn (which is included here), and the 'Monash' theme by Rob J Hyndman. Additional (free) fonts may be needed: 'Metropolis' prefers 'Fira', and 'IQSS' requires 'Libertinus'.

Data from the seven Australian Federal Elections (House of Representatives) between 2001 and 2019, and from the four Australian Censuses over the same period. Includes tools for visualizing and analysing the data, as well as imputing Census data for years in which a Census does not occur. This package incorporates data that is copyright Commonwealth of Australia (Australian Electoral Commission and Australian Bureau of Statistics) 2019.

The evolutionary model-based multiresponse approach (EMMA) is a novel methodology to process optimisation and product improvement. The approach is suitable to contexts in which the experimental cost and/or time limit the number of implementable trials.

Maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989); including inference and basic methods. Some alternative algorithms to estimate "H".

Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.

Generates time series based on mixture autoregressive models. Kang,Y.,Hyndman,R.,Li,F.(2020)<doi:10.1002/sam.11461>.

Provides tools for systematically exploring large quantities of temporal data across cyclic temporal granularities (deconstructions of time) by visualizing probability distributions. Cyclic time granularities can be circular, quasi-circular or aperiodic. 'gravitas' computes cyclic single-order-up or multiple-order-up granularities, check the feasibility of creating plots for any two cyclic granularities and recommend probability distributions plots for exploring periodicity in the data.

Implementation of the FASSTER model for forecasting time series with multiple seasonalities using switching states.

Provides methods for analysing and forecasting hierarchical and grouped time series. The available forecast methods include bottom-up, top-down, optimal combination reconciliation (Hyndman et al. 2011) <doi:10.1016/j.csda.2011.03.006>, and trace minimization reconciliation (Wickramasuriya et al. 2018) <doi:10.1080/01621459.2018.1448825>.

Outlier detection using leave-one-out kernel density estimates and extreme value theory. The bandwidth for kernel density estimates is computed using persistent homology, a technique in topological data analysis. Using peak-over-threshold method, a generalized Pareto distribution is fitted to the log of leave-one-out kde values to identify outliers.

Despite that several tests for normality in stationary processes have been proposed in the literature, consistent implementations of these tests in programming languages are limited. Four normality test are implemented. The Lobato and Velasco's, Epps, Psaradakis and Vavra, and the random projections tests for stationary process. Some other diagnostics such as, unit root test for stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. The package also offers residual diagnostic for linear time series models developed in several packages.

We provide an outlier robust alternative of the function ets() in the 'forecast' package of Hyndman and Khandakar (2008) <DOI:10.18637/jss.v027.i03>. For each method of a class of exponential smoothing variants we made a robust alternative. The class includes methods with a damped trend and/or seasonal components. The robust method is developed by robustifying every aspect of the original exponential smoothing variant. We provide robust forecasting equations, robust initial values, robust smoothing parameter estimation and a robust information criterion. The method is described in more detail in Crevits and Croux (2016) <DOI:10.13140/RG.2.2.11791.18080>.

The implemented method uses for smoothing bivariate thin plate splines, bivariate lasso-type regularization, and allows for both period and cohort effects. Thus the mortality rates are modelled as the sum of four components: a smooth bivariate function of age and time, smooth one-dimensional cohort effects, smooth one-dimensional period effects and random errors.

Methods for decomposing seasonal data: STR (a Seasonal-Trend decomposition procedure based on Regression) and Robust STR. In some ways, STR is similar to Ridge Regression and Robust STR can be related to LASSO. They allow for multiple seasonal components, multiple linear covariates with constant, flexible and seasonal influence. Seasonal patterns (for both seasonal components and seasonal covariates) can be fractional and flexible over time; moreover they can be either strictly periodic or have a more complex topology. The methods provide confidence intervals for the estimated components. The methods can be used for forecasting.

This is a modification of 'HDoutliers' package. The 'HDoutliers' algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation. However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. This package implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019) <arXiv:1908.04000> for detecting anomalies in high-dimensional data that addresses these limitations of 'HDoutliers' algorithm. We define an anomaly as an observation that deviates markedly from the majority with a large distance gap. An approach based on extreme value theory is used for the anomalous threshold calculation.

Provides 'ggplot2' graphics for analysing time series data. It aims to fit into the 'tidyverse' and grammar of graphics framework for handling temporal data.

Provides a 'tbl_ts' class (the 'tsibble') for temporal data in an data- and model-oriented format. The 'tsibble' provides tools to easily manipulate and analyse temporal data, such as filling in time gaps and aggregating over calendar periods.

Provides diverse datasets in the 'tsibble' data structure. These datasets are useful for learning and demonstrating how tidy temporal data can tidied, visualised, and forecasted.

Provides templates and functions to simplify the production and maintenance of curriculum vitae.