Earo Wang

Earo Wang

10 packages on CRAN

3 packages on GitHub

hts

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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>.

anomalous

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Methods for detecting anomalous time series.

anomalousACM

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Methods for detecting anomalous time series.

rwalkr

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Provides API to Melbourne pedestrian data in tidy data form.

sugrrants

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

tsibble

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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, aggregating over calendar periods, and etc.

eechidna

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Data from the six Australian Federal Elections (House of Representatives) between 2001 and 2016, 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) 2016.

forecast

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

fable

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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.

quokar

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Diagnostics methods for quantile regression models for detecting influential observations: robust distance methods for general quantile regression models; generalized Cook's distance and Q-function distance method for quantile regression models using aymmetric Laplace distribution. Reference of this method can be found in Luis E. Benites, V<c3><ad>ctor H. Lachos, Filidor E. Vilca (2015) <arXiv:1509.05099v1>; mean posterior probability and Kullback<e2><80><93>Leibler divergence methods for Bayes quantile regression model. Reference of this method is Bruno Santos, Heleno Bolfarine (2016) <arXiv:1601.07344v1>.

tsfeatures

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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.

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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.

visdat

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Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using 'ggplot2'.