Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/microprediction/timemachines
Predict time-series with one line of code.
https://github.com/microprediction/timemachines
prediction prediction-algorithm predictions predictive-modeling time-series time-series-analysis timeseries timeseries-analysis timeseries-data timeseries-forecasting
Last synced: 6 days ago
JSON representation
Predict time-series with one line of code.
- Host: GitHub
- URL: https://github.com/microprediction/timemachines
- Owner: microprediction
- License: mit
- Created: 2021-01-04T15:38:41.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-10-21T14:44:48.000Z (4 months ago)
- Last Synced: 2025-02-07T09:05:46.283Z (13 days ago)
- Topics: prediction, prediction-algorithm, predictions, predictive-modeling, time-series, time-series-analysis, timeseries, timeseries-analysis, timeseries-data, timeseries-forecasting
- Language: Python
- Homepage: https://www.microprediction.com/blog/popular-timeseries-packages
- Size: 6.08 MB
- Stars: 417
- Watchers: 12
- Forks: 53
- Open Issues: 78
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome_time_series_in_python - timemachines
- awesome-time-series - timemachines - series forecasting. (📦 Packages / Python)
- StarryDivineSky - microprediction/timemachines
README
# timemachines ([docs](https://microprediction.github.io/timemachines/) and [leaderboards](https://microprediction.github.io/timeseries-elo-ratings/html_leaderboards/overall.html))  
Univariate prediction functions from diverse packages supported in a simple stateless pure function syntax, mosty for benchmarking and application-specific selection purposes. See [basic usage](https://github.com/microprediction/timemachines/blob/main/examples/basic_usage/run_skater.py). Briefly: if `yt` is a list of floats we can feed them one at a time to a skater like so:
from timemachines.skaters.somepackage.somevariety import something as f
for yt in y:
xt, xt_std, s = f(y=yt, s=s, k=3)
This emits a k-vector `xt` of forecasts, and corresponding k-vector `xt_std` of estimated standard errors, and the posterior state `s` needed for the next call. See [skaters](https://microprediction.github.io/timemachines/skaters) for choices of `somepackage`, `somevariety` and `something`. You can also ensemble, compose, bootstrap and do other things with one line of code. The `f` is called a `skater`. These are ([documented](https://microprediction.github.io/timemachines/) and [assessed](https://microprediction.github.io/timeseries-elo-ratings/html_leaderboards/overall.html)). See [why](https://microprediction.github.io/timemachines/why) for motivation for doing things in **walk-forward incremental** functional fashion with **one line of code**.
Test for the following packages are expected to be working, and their use in this context reliable:
-  Statsmodels
- 
- 
- 
- 
- 
- 
- 
Test for the following packages are not necessarily expected to be working:
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
See the [docs](https://microprediction.github.io/timemachines/).
## What's a "skater"?
More abstractly:
$$
f : (y_t, state; k) \mapsto ( [\hat{y}(t+1),\hat{y}(t+2),\dots,\hat{y}(t+k) ], [\sigma(t+1),\dots,\sigma(t+k)], posterior\ state))
$$
where $\sigma(t+l)$ estimates the standard error of the prediction $\hat{y}(t+l)$. If you prefer an legitimate (i.e. stateful) state machine, that's fine but see see [FAQ](https://github.com/microprediction/timemachines/blob/main/FAQ.md) question 1 for how to quickly roll this yourself.
### Skater function conventions
See [docs/interface](https://microprediction.github.io/timemachines/interface) for description of skater inputs and outputs. Briefly:
x, w, s = f( y:Union[float,[float]], # Contemporaneously observerd data,
# ... including exogenous variables in y[1:], if any.
s=None, # Prior state
k:float=1, # Number of steps ahead to forecast. Typically integer.
a:[float]=None, # Variable(s) known in advance, or conditioning
t:float=None, # Time of observation (epoch seconds)
e:float=None, # Non-binding maximal computation time ("e for expiry"), in seconds
r:float=None) # Hyper-parameters ("r" stands for for hype(r)-pa(r)amete(r)s)
### Contributing
- See [TFRO.md](https://github.com/microprediction/monteprediction/blob/main/TFRO.md)
- See [CONTRIBUTE.md](https://github.com/microprediction/timemachines/blob/main/CONTRIBUTE.md) and [good first issues](https://github.com/microprediction/timemachines/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22).
- See the suggested steps for a [capstone project](https://microprediction.github.io/timemachines/capstone.html).
### Getting live help
- Office hours are mentioned [here](https://github.com/microprediction).
- Discord channle is mentioned [here](https://github.com/microprediction).
See also [FAQ](https://github.com/microprediction/timemachines/blob/main/FAQ.md).
### Install [instructions](https://github.com/microprediction/timemachines/blob/main/INSTALL.md)
Oh what a mess the Python timeseries ecosystem is. So packages are not installed by default. See the methodical [install instructions](https://github.com/microprediction/timemachines/blob/main/INSTALL.md) and be incremental for best results. The infamous [xkcd cartoon](https://xkcd.com/1987/) really does describe the alternative quite well.
### Links to packages used
Skaters draw on functionality from [popular python time-series packages](https://www.microprediction.com/blog/popular-timeseries-packages) like [river](https://github.com/online-ml/river), [pydlm](https://github.com/wwrechard/pydlm), [tbats](https://github.com/intive-DataScience/tbats), [pmdarima](http://alkaline-ml.com/pmdarima/), [statsmodels.tsa](https://www.statsmodels.org/stable/tsa.html), [neuralprophet](https://neuralprophet.com/), Facebook [Prophet](https://facebook.github.io/prophet/),
Uber's [orbit](https://eng.uber.com/orbit/), Facebook's [greykite](https://engineering.linkedin.com/blog/2021/greykite--a-flexible--intuitive--and-fast-forecasting-library) and more. See the [docs](https://microprediction.github.io/timemachines/).
### Cite
Thanks
@electronic{cottontimemachines,
title = {{Timemachines: A Python Package for Creating and Assessing Autonomous Time-Series Prediction Algorithms}},
year = {2021},
author = {Peter Cotton},
url = {https://github.com/microprediction/timemachines}
}
or something [here](https://github.com/microprediction/microprediction/blob/master/CITE.md).