Scalable machine learning based time series forecasting
Project description
mlforecast
Install
PyPI
pip install mlforecast
If you want to perform distributed training, you can instead use
pip install "mlforecast[distributed]", which will also install
dask. Note that you’ll also need to install either
LightGBM
or
XGBoost.
conda-forge
conda install -c conda-forge mlforecast
Note that this installation comes with the required dependencies for the
local interface. If you want to perform distributed training, you must
install dask (conda install -c conda-forge dask) and either
LightGBM
or
XGBoost.
How to use
The following provides a very basic overview, for a more detailed description see the documentation.
Data setup
Store your time series in a pandas dataframe in long format, that is, each row represents an observation for a specific serie and timestamp.
from mlforecast.utils import generate_daily_series
series = generate_daily_series(
n_series=20,
max_length=100,
n_static_features=1,
static_as_categorical=False,
with_trend=True
)
series.head()
| ds | y | static_0 | |
|---|---|---|---|
| unique_id | |||
| id_00 | 2000-01-01 | 1.751917 | 72 |
| id_00 | 2000-01-02 | 9.196715 | 72 |
| id_00 | 2000-01-03 | 18.577788 | 72 |
| id_00 | 2000-01-04 | 24.520646 | 72 |
| id_00 | 2000-01-05 | 33.418028 | 72 |
Models
Next define your models. If you want to use the local interface this can
be any regressor that follows the scikit-learn API. For distributed
training there are LGBMForecast and XGBForecast.
import lightgbm as lgb
import xgboost as xgb
from sklearn.ensemble import RandomForestRegressor
models = [
lgb.LGBMRegressor(),
xgb.XGBRegressor(),
RandomForestRegressor(random_state=0),
]
Forecast object
Now instantiate a MLForecast object with the models and the features
that you want to use. The features can be lags, transformations on the
lags and date features. The lag transformations are defined as
numba jitted functions that transform an
array, if they have additional arguments you can either supply a tuple
(transform_func, arg1, arg2, …) or define new functions fixing the
arguments. You can also define differences to apply to the series before
fitting that will be restored when predicting.
from mlforecast import MLForecast
from numba import njit
from window_ops.expanding import expanding_mean
from window_ops.rolling import rolling_mean
@njit
def rolling_mean_28(x):
return rolling_mean(x, window_size=28)
fcst = MLForecast(
models=models,
freq='D',
lags=[7, 14],
lag_transforms={
1: [expanding_mean],
7: [rolling_mean_28]
},
date_features=['dayofweek'],
differences=[1],
)
Training
To compute the features and train the models call fit on your
Forecast object. Here you have to specify the columns that:
- Identify each serie (
id_col). If the series identifier is the index you can specifyid_col='index' - Contain the timestamps (
time_col). Can also be integers if your data doesn’t have timestamps. - Are the series values (
target_col) - Are static (
static_features). These are features that don’t change over time and can be repeated when predicting.
fcst.fit(series, id_col='index', time_col='ds', target_col='y', static_features=['static_0'])
MLForecast(models=[LGBMRegressor, XGBRegressor, RandomForestRegressor], freq=<Day>, lag_features=['lag-7', 'lag-14', 'expanding_mean_lag-1', 'rolling_mean_28_lag-7'], date_features=['dayofweek'], num_threads=1)
Predicting
To get the forecasts for the next n days call predict(n) on the
forecast object. This will automatically handle the updates required by
the features using a recursive strategy.
predictions = fcst.predict(14)
predictions
| ds | LGBMRegressor | XGBRegressor | RandomForestRegressor | |
|---|---|---|---|---|
| unique_id | ||||
| id_00 | 2000-04-04 | 69.082830 | 67.761337 | 68.184016 |
| id_00 | 2000-04-05 | 75.706024 | 74.588699 | 75.470680 |
| id_00 | 2000-04-06 | 82.222473 | 81.058289 | 82.846249 |
| id_00 | 2000-04-07 | 89.577638 | 88.735947 | 90.201271 |
| id_00 | 2000-04-08 | 44.149095 | 44.981384 | 46.096322 |
| ... | ... | ... | ... | ... |
| id_19 | 2000-03-23 | 30.236012 | 31.949095 | 32.656369 |
| id_19 | 2000-03-24 | 31.308269 | 32.765919 | 33.624488 |
| id_19 | 2000-03-25 | 32.788550 | 33.628864 | 34.581486 |
| id_19 | 2000-03-26 | 34.086976 | 34.508457 | 35.553173 |
| id_19 | 2000-03-27 | 34.288968 | 35.411613 | 36.526505 |
280 rows × 4 columns
Visualize results
import matplotlib.pyplot as plt
import pandas as pd
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(12, 6), gridspec_kw=dict(hspace=0.3))
for i, (cat, axi) in enumerate(zip(series.index.categories, ax.flat)):
pd.concat([series.loc[cat, ['ds', 'y']], predictions.loc[cat]]).set_index('ds').plot(ax=axi)
axi.set(title=cat, xlabel=None)
if i % 2 == 0:
axi.legend().remove()
else:
axi.legend(bbox_to_anchor=(1.01, 1.0))
fig.savefig('figs/index.png', bbox_inches='tight')
plt.close()
Sample notebooks
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for mlforecast-0.6.0-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 1d0047dd164c50694cb9d7ebbeaa56243c79c151efb0a3ff63fc1dba6dad4c32 |
|
| MD5 | de331acbd734967aea5781e8449cb789 |
|
| BLAKE2b-256 | 15026752419a57cd16209bef04df707264bed8aa5f1f62fc717a83b7ccd3f7ad |
