A python package for multi-variate time series prediction
Project description
# fireTS #
`fireTS` is a sklean style package for multi-variate time-series prediction. I
developed this package when writing [this
paper](http://ceur-ws.org/Vol-2148/paper16.pdf). The paper introduced two
methods to perform multi-step prediction: recursive method and direct method.
`fireTS.models.NARX` model is trying to train a one-step-ahead-prediction model
and make multi-step prediction recursively given the future exogenous inputs.
`fireTS.models.DirectAutoRegressor` model is trying to train a
multi-step-head-prediction model directly. No future exogenous inputs are
required to make the multi-step prediction.
## Installation ##
It is highly recommended to use `pip` to install `fireTS`, follow this
[link](https://pip.pypa.io/en/stable/installing/) to install pip.
After pip is installed,
```
pip install firets
```
To get the latest development version,
```
git clone
```
* Dependencies
- numpy
- sklearn
## Quick Start ##
`fireTS` is a sklean style package for multi-variate time-series prediction. I
developed this package when writing [this
paper](http://ceur-ws.org/Vol-2148/paper16.pdf). The paper introduced two
methods to perform multi-step prediction: recursive method and direct method.
`fireTS.models.NARX` model is trying to train a one-step-ahead-prediction model
and make multi-step prediction recursively given the future exogenous inputs.
`fireTS.models.DirectAutoRegressor` model is trying to train a
multi-step-head-prediction model directly. No future exogenous inputs are
required to make the multi-step prediction.
## Installation ##
It is highly recommended to use `pip` to install `fireTS`, follow this
[link](https://pip.pypa.io/en/stable/installing/) to install pip.
After pip is installed,
```
pip install firets
```
To get the latest development version,
```
git clone
```
* Dependencies
- numpy
- sklearn
## Quick Start ##
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