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https://github.com/bearloga/maltese
Little R utility package for making time series data more machine learning-friendly
https://github.com/bearloga/maltese
forecasting machine-learning r r-package rstats time-series
Last synced: about 2 months ago
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Little R utility package for making time series data more machine learning-friendly
- Host: GitHub
- URL: https://github.com/bearloga/maltese
- Owner: bearloga
- Created: 2017-01-28T00:02:01.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2020-03-06T17:42:53.000Z (over 4 years ago)
- Last Synced: 2024-07-23T08:39:00.571Z (2 months ago)
- Topics: forecasting, machine-learning, r, r-package, rstats, time-series
- Language: R
- Homepage: https://bearloga.github.io/maltese/neuralnet.html
- Size: 813 KB
- Stars: 50
- Watchers: 7
- Forks: 8
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# maltese: machine learning for time series
[](https://travis-ci.org/bearloga/maltese)
## Installing
```R
# install.packages("remotes")
remotes::install_github("bearloga/maltese")
```
## Example
### Data
The included dataset is a tidy time series of pageviews for R's article on English Wikipedia from 2015-10-01 to 2017-01-30.
```R
library(maltese)
head(r_enwiki)
```
|date | pageviews|
|:----------|---------:|
|2015-10-01 | 3072|
|2015-10-02 | 2575|
|2015-10-03 | 1431|
|2015-10-04 | 1540|
|2015-10-05 | 3041|
|2015-10-06 | 3695|
We can use `mlts_transform` to convert the data into a machine learning-friendly format with a 7-day lag:
```R
mlts <- mlts_transform(
r_enwiki, date, pageviews,
p = 7, # how many previous points of data to use as features
granularity = "day", # optional, can be automatically detected,
extras = TRUE, extrasAsFactors = TRUE # FALSE by default :D
)
head(mlts)
```
|dt | y|mlts_extras_monthday |mlts_extras_weekday |mlts_extras_week |mlts_extras_month |mlts_extras_year | mlts_lag_1| mlts_lag_2| mlts_lag_3| mlts_lag_4| mlts_lag_5| mlts_lag_6| mlts_lag_7|
|:----------|----:|:--------------------|:-------------------|:----------------|:-----------------|:----------------|----------:|----------:|----------:|----------:|----------:|----------:|----------:|
|2015-10-08 | 3278|8 |Thursday |41 |October |2015 | 3385| 3695| 3041| 1540| 1431| 2575| 3072|
|2015-10-09 | 2886|9 |Friday |41 |October |2015 | 3278| 3385| 3695| 3041| 1540| 1431| 2575|
|2015-10-10 | 1692|10 |Saturday |41 |October |2015 | 2886| 3278| 3385| 3695| 3041| 1540| 1431|
|2015-10-11 | 1902|11 |Sunday |41 |October |2015 | 1692| 2886| 3278| 3385| 3695| 3041| 1540|
|2015-10-12 | 3030|12 |Monday |41 |October |2015 | 1902| 1692| 2886| 3278| 3385| 3695| 3041|
|2015-10-13 | 3245|13 |Tuesday |41 |October |2015 | 3030| 1902| 1692| 2886| 3278| 3385| 3695|
### Results
See [the vignette](https://bearloga.github.io/maltese/neuralnet.html) for a detailed walkthrough.
## Additional Information
Users of _maltese_ may also be interested in _[timetk](https://business-science.github.io/timetk/)_ ([available on CRAN](https://cran.r-project.org/package=timetk)) which provides several utility functions for working with and manipulating time series data into a ML-friendly form.
Please note that this project is released with a [Contributor Code of Conduct](https://github.com/bearloga/maltese/blob/master/CONDUCT.md). By participating in this project you agree to abide by its terms.