A Wrapper around MLForecast.
Project description
Seánce v0.0.5
A simple wrapper around Nixtla's MLForecast aimed at streamlining plug-and-play forecasting.
A general pattern is to optimize then forecast such as:
from mlforecast.utils import generate_daily_series
series = generate_daily_series(
n_series=20,
max_length=100,
min_length=50,
with_trend=True
)
from Seance.Optimizer import Optimize
opt = Optimize(series,
target_column='y',
date_column='ds',
id_column='unique_id',
freq='D',
seasonal_period=7,
test_size=10,
# ar_lags=[list(range(1, 8))], #by default this will be done based on seasonal period
metric='smape',
n_trials=100)
#returns an optuna study obj
best_params, study = opt.fit(seed=1)
optuna plotting
import optuna
optuna.visualization.matplotlib.plot_param_importances(study)
Here we can see the most important parameter is (unsurprisingly) the number of lags. Followd by decay which controls the 'forgetfulness' of the basis functions.
optuna.visualization.matplotlib.plot_optimization_history(study)
passing off best params for forecasts
from Seance.Forecaster import Forecaster
seance = Forecaster()
output = seance.fit(series,
target_column='y',
date_column='ds',
id_column='unique_id',
freq='D',
**best_params)
predicted = seance.predict(24)
quick plot of the forecasts
import matplotlib.pyplot as plt
plot_ser = np.append(series[series['unique_id'] == 'id_00']['y'].values,
predicted[predicted['unique_id'] == 'id_00']['LGBMRegressor'].values)
plt.plot(plot_ser)
plt.vlines(x=len(plot_ser) - 24, ymin=0, ymax=max(plot_ser), linestyle='dashed', color='red')
plt.show()
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