A python package for multi-variate time series prediction

## Project description

# fireTS

`fireTS`

is a sklean style package for multi-variate time-series prediction. Here is a simple code snippet to showcase the awesome features provided by `fireTS`

package.

from fireTS.models import NARX, DirectAutoRegressor from sklearn.ensemble import RandomForestRegressor from xgboost import XGBRegressor import numpy as np # Random training data x = np.random.randn(100, 2) y = np.random.randn(100) # Build a non-linear autoregression model with exogenous inputs # using Random Forest regression as the base model mdl1 = NARX( RandomForestRegressor(n_estimators=10), auto_order=2, exog_order=[2, 2], exog_delay=[1, 1]) mdl1.fit(x, y) ypred1 = mdl1.predict(x, y, step=3) # Build a general autoregression model and make multi-step prediction directly # using XGBRegressor as the base model mdl2 = DirectAutoRegressor( XGBRegressor(n_estimators=10), auto_order=2, exog_order=[2, 2], exog_delay=[1, 1], pred_step=3) mdl2.fit(x, y) ypred2 = mdl2.predict(x, y)

`sklearn`

style API. The package provides`fit`

and`predict`

methods, which is very similar to`sklearn`

package.- Plug-and-go. You are able to plug in any machine learning regression algorithms provided in
`sklearn`

package and build a time-series forecasting model. - Create the lag features for you by specifying the autoregression order
`auto_order`

, the exogenous input order`exog_order`

, and the exogenous input delay`exog_delay`

. - Support multi-step prediction. The package can make multi-step prediction in two different ways: recursive way and direct way.
`NARX`

model is to build a one-step-ahead-predictive model, and the model will be used recursively to make multi-step prediction (future exogenous input information is needed).`DirectAutoRegressor`

makes multi-step prediction directly (no future exogenous input information is needed) by specifying the prediction step in the constructor. - Support grid search to tune the hyper-parameters of the base model (cannot do grid search on the orders and delays of the time series model for now).

I developed this package when writing this paper. It is really handy to generate lag features and leverage various regression algorithms provided by sklearn to build non-linear multi-variate time series models. The API can also be used to build deep neural network models to make time-series prediction. The paper used this package to build LSTM models and make multi-step predictions.

The documentation can be found here. The documentation provides the mathematical equations of each model. It is highly recommended to read the documentation before using the model.

### Nonlinear AutoRegression with eXogenous (NARX) model

`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.

Given the output time series to predict `y(t)`

and exogenous inputs `X(t)`

The model will generate target and features as follows:

Target | Features |
---|---|

y(t + 1) | y(t), y(t - 1), ..., y(t - p + 1), X(t - d), X(t - d - 1), ..., X(t - d - q + 1) |

where p is the autogression order `auto_order`

, q is the exogenous input order `exog_order`

, d is the exogenous delay `exog_delay`

.

NARX model can make any step ahead prediction given the future exogenous inputs. To make multi-step prediction, set the `step`

in the `predict`

method.

### Direct Autoregressor

`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.

Given the output time series to predict `y(t)`

and exogenous inputs `X(t)`

The model will generate target and features as follows:

Target | Features |
---|---|

y(t + k) | y(t), y(t - 1), ..., y(t - p + 1), X(t - d), X(t - d - 1), ..., X(t - d - q + 1) |

where p is the autogression order `auto_order`

, q is the exogenous input order `exog_order`

, d is the exogenous delay `exog_delay`

, k is the prediction step `pred_step`

.

Direct autoregressor does not require future exogenous input information to make multi-step prediction. Its `predict`

method cannot specify prediction step.

## Installation

**NOTE**: Only python3 is supported.

It is highly recommended to use `pip`

to install `fireTS`

, follow this
link to install pip.

After pip is installed,

```
pip install fireTS
```

To get the latest development version,

```
git clone https://github.com/jxx123/fireTS.git
cd fireTS
pip install -e .
```

## Quick Start

- Use
`RandomForestRegressor`

as base model to build a`NARX`

model

from fireTS.models import NARX from sklearn.ensemble import RandomForestRegressor import numpy as np x = np.random.randn(100, 1) y = np.random.randn(100) mdl = NARX(RandomForestRegressor(), auto_order=2, exog_order=[2], exog_delay=[1]) mdl.fit(x, y) ypred = mdl.predict(x, y, step=3)

- Use
`RandomForestRegressor`

as base model to build a`DirectAutoRegressor`

model

from fireTS.models import DirectAutoRegressor from sklearn.ensemble import RandomForestRegressor import numpy as np x = np.random.randn(100, 1) y = np.random.randn(100) mdl = DirectAutoRegressor(RandomForestRegressor(), auto_order=2, exog_order=[2], exog_delay=[1], pred_step=3) mdl.fit(x, y) ypred = mdl.predict(x, y)

- Usage of grid search

from fireTS.models import NARX from sklearn.ensemble import RandomForestRegressor import numpy as np x = np.random.randn(100, 1) y = np.random.randn(100) # DirectAutoRegressor can do grid search as well mdl = NARX(RandomForestRegressor(), auto_order=2, exog_order=[2], exog_delay=[1]) # Grid search para_grid = {'n_estimators': [10, 30, 100]} mdl.grid_search(x, y, para_grid, verbose=2) # Best hyper-parameters are set after grid search, print the model to see the difference print(mdl) # Fit the model and make the prediction mdl.fit(x, y) ypred = mdl.predict(x, y, step=3)

The examples folder provides more realistic examples. The example1 and example2 use the data simulated by simglucose pakage to fit time series model and make multi-step prediction.

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