Introduction | Sections | Title formats | Metropolis title formats | Elements | Typography | Font feature test | Lists | Animation (using \LaTeX\ ) | Animation (using RMarkdown, plus one \LaTeX\ trick) | Figures (using \LaTeX) | Tables (using \LaTeX}) | Blocks | Math | Line plots | Bar charts | Quotes | References | Notes | Conclusion | Summary | Backup slides | R Appendix: R Figure Example | R Appendix: R Table Example

About gratis | Generate diverse time series | Generate multiple seasonal time series | Generate time series with targetted features | ARIMA and ETS models

Contextual semantics: index and key | Data pipeline | Irregular time interval

Overview | When to use FASSTER | Model specification | Basic formula syntax | Model components | Trend terms | Seasonal terms | ARMA terms | Exogenous regressors | State switching | Example: Gas production with structural break | Fitting a basic model | Decomposition | Forecasting | Handling missing values | Advanced features | Multiple switching levels | Combining components | Adding ARMA terms | Parameter estimation | Summary

vital objects | Plots | Life tables and life expectancy | Smoothing | Lee-Carter models | Functional data models | Coherent functional data models | References

Introduction | Exponential smoothing | Point forecasts for all methods | Innovations state space models | State space models for all exponential smoothing methods | Estimation | Model selection | Automatic forecasting | ARIMA models | Choosing the model order using unit root tests and the AIC | A step-wise procedure for traversing the model space | Comparisons with exponential smoothing | The forecast package | Implementation of the automatic exponential smoothing algorithm | The HoltWinters() function | Implementation of the automatic ARIMA algorithm | The forecast() function | Other functions | Bibliography

tsfeatures | Installation | Usage | List of features | acf_features | arch_stat | autocorr_features | binarize_mean | compengine feature set | crossing_points | dist_features | entropy | firstzero_ac | flat_spots | heterogeneity | holt_parameters and hw_parameters | hurst | lumpiness and stability | max_level_shift, max_var_shift and max_kl_shift | nonlinearity | pacf_features | pred_features | scal_features | station_features | stl_features | unitroot_kpss and unitroot_pp | zero_proportion | Reproducing papers | Hyndman, Wang and Laptev (ICDM 2015) | Kang, Hyndman & Smith-Miles (IJF 2017) | Resources | License

Introduction | Data sets | Model selection exercise | bonds | usnetelec | ukcars | visitors

Research | Teaching | Teaching performance | Teaching innovations | Supervision | Engagement | Engagement with Industry | Engagement with Government | Engagement with Public | Engagement with Professional Community | Engagement with Schools | Contributions to enhancing the employability of graduates | Engagement/leadership within one’s profession or discipline | Leadership

Total output for 2024

Data simulation | Time series cross-validation | Conformal prediction | Classical split conformal prediction (SCP) | Adaptive conformal prediction (ACP) | Conformal PID control (PID) | Multistep-ahead conformal prediction (AcMCP) | Coverage and width of prediction intervals

Introduction | Getting started | Example with multiple seasonality | Tuning an STR decomposition | Robust STR decomposition | Another example with multiple seasonality | A complex example | A forecasting example | Final notes

Mortality model | Fertility model | Net migration model | Population simulation | References

Introduction | Why cricketdata | Installation | Functions | fetch_cricinfo() | fetch_player_id | fetch_player_data | fetch_cricsheet() | player_meta | fetch_player_meta() | update_player_meta() | References

Alyssa Healy's WBBL batting record | Tidying the data | Making a plot | Dismissals by ball number | Making plots | “Wiggly bois”: Visualising team strike rates

Women's T20 bowling data | Australian men's ODI data by innings | Indian test fielding data | Meg Lanning's ODI batting

Introduction | Scales | Use in conjunction with group_by | More geoms | Summary | Reference

tscompdata | Installation | Usage | NN3 data | NN5 data | NNGC1 data | GEFCOM2012 load data | GEFCOM2012 temperature data | GEFCOM2012 wind power data | Mcomp: M1 data | Mcomp: M3 data | Tcomp: tourism forecasting competition data | Sources | License

Creating a CV | The YAML header | The document body | Detailed entries | Brief entries | Bibliography entries

Spreadsheets and data sets | Google scholar | Bibliography files | R packages

Introduction | Data sets | Graphical summaries | Time plots and time series patterns | Seasonal plots | Scatterplots | Numerical summaries | Univariate statistics | Bivariate statistics | Autocorrelation | Forecast accuracy | Transformations and adjustments | Mathematical transformations | Calendar adjustments

Synthetic data | Plotting | Features | Anomalies

Creating a vitae template | Converting a CV into a Pandoc template | Adding template specific code for displaying CV entries | brief_entries | detailed_entries | Using the template with rmarkdown | Contributing to the vitae package

An example

Copying and archiving old polls

Introduction | Joining election and Census data | Election results | Which party won the election? | Which party received the most first preference votes? | Downloading and plotting the two party preferred vote | Census data | Income and unemployment by state | Ageing population | Maps of the Australian electoral divisions | Attaching election results | Modelling election results using Census data | Imputing Census data for the elections that do not have a Census that matches exactly | For more details on how the data in this package was obtained...

Combining transformations | Custom transformations | Forecast means and medians

The model interface | Minimum implementation of a model | The model function | The specials | The training function | Methods for models

Installation | Usage | License