# tsfeatures v1.0.1

Monthly downloads

## Time Series Feature Extraction

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.

## Readme

# tsfeatures

The R package *tsfeatures* provides methods for extracting various
features from time series data.

## Installation

You can install the **stable** version on R
CRAN.

```
install.packages('tsfeatures', dependencies = TRUE)
```

You can install the **development** version from
Github with:

```
# install.packages("devtools")
devtools::install_github("robjhyndman/tsfeatures")
```

## Usage

```
library(tsfeatures)
mylist <- list(sunspot.year, WWWusage, AirPassengers, USAccDeaths)
myfeatures <- tsfeatures(mylist)
myfeatures
#> # A tibble: 4 x 20
#> frequency nperiods seasonal_period trend spike linearity curvature
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 1 0.125 2.10e-5 3.58 1.11
#> 2 1 0 1 0.985 3.01e-8 4.45 1.10
#> 3 12 1 12 0.989 2.12e-8 11.0 1.10
#> 4 12 1 12 0.796 9.67e-7 -2.13 2.85
#> # ... with 13 more variables: e_acf1 <dbl>, e_acf10 <dbl>, entropy <dbl>,
#> # x_acf1 <dbl>, x_acf10 <dbl>, diff1_acf1 <dbl>, diff1_acf10 <dbl>,
#> # diff2_acf1 <dbl>, diff2_acf10 <dbl>, seasonal_strength <dbl>,
#> # peak <dbl>, trough <dbl>, seas_acf1 <dbl>
```

## License

This package is free and open source software, licensed under GPL-3.

## Functions in tsfeatures

Name | Description | |

hurst | Hurst coefficient | |

ac_9 | Autocorrelation at lag 9. Included for completion and consistency. | |

compengine | CompEngine feature set | |

binarize_mean | Converts an input vector into a binarized version from software package hctsa | |

acf_features | Autocorrelation-based features | |

fluctanal_prop_r1 | Implements fluctuation analysis from software package hctsa | |

entropy | Spectral entropy of a time series | |

max_level_shift | Time series features based on sliding windows | |

unitroot_kpss | Unit Root Test Statistics | |

firstmin_ac | Time of first minimum in the autocorrelation function from software package hctsa | |

crossing_points | Number of crossing points | |

dist_features | The distribution feature set from software package hctsa | |

embed2_incircle | Points inside a given circular boundary in a 2-d embedding space from software package hctsa | |

pred_features | The prediction feature set from software package hctsa | |

lumpiness | Time series features based on tiled windows | |

sampen_first | Second Sample Entropy of a time series from software package hctsa | |

firstzero_ac | The first zero crossing of the autocorrelation function from software package hctsa | |

yahoo_data | Yahoo server metrics | |

walker_propcross | Simulates a hypothetical walker moving through the time domain from software package hctsa | |

heterogeneity | Heterogeneity coefficients | |

sampenc | Second Sample Entropy from software package hctsa | |

flat_spots | Number of flat spots | |

histogram_mode | Mode of a data vector from software package hctsa | |

holt_parameters | Parameter estimates of Holt's linear trend method | |

motiftwo_entro3 | Local motifs in a binary symbolization of the time series from software package hctsa | |

nonlinearity | Nonlinearity coefficient | |

outlierinclude_mdrmd | How median depend on distributional outliers from software package hctsa | |

pacf_features | Partial autocorrelation-based features | |

spreadrandomlocal_meantaul | Bootstrap-based stationarity measure from software package hctsa | |

scal_features | The scaling feature set from software package hctsa | |

station_features | The stationarity feature set from software package hctsa | |

trev_num | Normalized nonlinear autocorrelation, the numerator of the trev function of a time series from software package hctsa | |

arch_stat | ARCH LM Statistic | |

as.list.mts | Convert mts object to list of time series | |

tsfeatures | Time series feature matrix | |

std1st_der | Standard deviation of the first derivative of the time series from software package hctsa | |

stl_features | Strength of trend and seasonality of a time series | |

autocorr_features | The autocorrelation feature set from software package hctsa | |

localsimple_taures | The first zero crossing of the autocorrelation function of the residuals from Simple local time-series forecasting from software package hctsa | |

No Results! |

## Vignettes of tsfeatures

Name | ||

tsfeatures.Rmd | ||

No Results! |

## Last month downloads

## Details

License | GPL-3 |

LazyData | true |

ByteCompile | true |

URL | https://pkg.robjhyndman.com/tsfeatures/ |

BugReports | https://github.com/robjhyndman/tsfeatures/issues/ |

RoxygenNote | 6.1.1 |

VignetteBuilder | knitr |

Encoding | UTF-8 |

NeedsCompilation | no |

Packaged | 2019-04-16 06:30:44 UTC; mitchell |

Repository | CRAN |

Date/Publication | 2019-04-16 13:02:47 UTC |

suggests | dplyr , GGally , ggplot2 , knitr , Mcomp , rmarkdown , testthat , tidyr |

imports | ForeCA , forecast (>= 8.3) , fracdiff , furrr , future , purrr , RcppRoll (>= 0.2.2) , stats , tibble , tseries , urca |

depends | R (>= 3.2.3) |

Contributors | Earo Wang, Nikolay Laptev, Yanfei Kang, Mitchell O'Hara-Wild, Thiyanga Talagala, Souhaib Ben Taieb, Yangzhuoran Yang, Pablo Montero-Manso, Cao Hanqing, D K Lake, J R Moorman |

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