scalecast

0

The practitioner's forecasting library

Analytics

python
time-series
machine-learning
data-science

Scalecast

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About

Scalecast helps you forecast time series. Here is how to initiate its main object:

from scalecast.Forecaster import Forecaster

f = Forecaster(
    y = array_of_values,
    current_dates = array_of_dates,
    future_dates=fcst_horizon_length,
    test_length = 0, # do you want to test all models? if so, on how many or what percent of observations?
    cis = False, # evaluate conformal confidence intervals for all models?
    metrics = ['rmse','mape','mae','r2'], # what metrics to evaluate over the validation/test sets?
)

Uniform ML modeling (with models from a diverse set of libraries, including scikit-learn, statsmodels, and tensorflow), reporting, and data visualizations are offered through the Forecaster and MVForecaster interfaces. Data storage and processing then becomes easy as all applicable data, predictions, and many derived metrics are contained in a few objects with much customization available through different modules. Feature requests and issue reporting are welcome! Don't forget to leave a star!⭐

Documentation

Popular Features

  1. Easy LSTM Modeling: setting up an LSTM model for time series using tensorflow is hard. Using scalecast, it's easy. Many tutorials and Kaggle notebooks that are designed for those getting to know the model use scalecast (see the aritcle).
f.set_estimator('lstm')
f.manual_forecast(
    lags=36,
    batch_size=32,
    epochs=15,
    validation_split=.2,
    activation='tanh',
    optimizer='Adam',
    learning_rate=0.001,
    lstm_layer_sizes=(100,)*3,
    dropout=(0,)*3,
)
  1. Auto lag, trend, and seasonality selection:
f.auto_Xvar_select( # iterate through different combinations of covariates
    estimator = 'lasso', # what estimator?
    alpha = .2, # estimator hyperparams?
    monitor = 'ValidationMetricValue', # what metric to monitor to make decisions?
    cross_validate = True, # cross validate
    cvkwargs = {'k':3}, # 3 folds
)
  1. Hyperparameter tuning using grid search and time series cross validation:
from scalecast import GridGenerator

GridGenerator.get_example_grids()
models = ['ridge','lasso','xgboost','lightgbm','knn']
f.tune_test_forecast(
    models,
    limit_grid_size = .2,
    feature_importance = True, # save pfi feature importance for each model?
    cross_validate = True, # cross validate? if False, using a seperate validation set that the user can specify
    rolling = True, # rolling time series cross validation?
    k = 3, # how many folds?
)
  1. Plotting results: plot test predictions, forecasts, fitted values, and more.
import matplotlib.pyplot as plt

fig, ax = plt.subplots(2,1, figsize = (12,6))
f.plot_test_set(models=models,order_by='TestSetRMSE',ax=ax[0])
f.plot(models=models,order_by='TestSetRMSE',ax=ax[1])
plt.show()
  1. Pipelines that include transformations, reverting, and backtesting:
from scalecast import GridGenerator
from scalecast.Pipeline import Transformer, Reverter, Pipeline
from scalecast.util import find_optimal_transformation, backtest_metrics

def forecaster(f):
    models = ['ridge','lasso','xgboost','lightgbm','knn']
    f.tune_test_forecast(
        models,
        limit_grid_size = .2, # randomized grid search on 20% of original grid sizes
        feature_importance = True, # save pfi feature importance for each model?
        cross_validate = True, # cross validate? if False, using a seperate validation set that the user can specify
        rolling = True, # rolling time series cross validation?
        k = 3, # how many folds?
    )

transformer, reverter = find_optimal_transformation(f) # just one of several ways to select transformations for your series

pipeline = Pipeline(
    steps = [
        ('Transform',transformer),
        ('Forecast',forecaster),
        ('Revert',reverter),
    ]
)

f = pipeline.fit_predict(f)
backtest_results = pipeline.backtest(f)
metrics = backtest_metrics(backtest_results)
  1. Model stacking: There are two ways to stack models with scalecast, with the StackingRegressor from scikit-learn or using its own stacking procedure.
from scalecast.auxmodels import auto_arima

f.set_estimator('lstm')
f.manual_forecast(
    lags=36,
    batch_size=32,
    epochs=15,
    validation_split=.2,
    activation='tanh',
    optimizer='Adam',
    learning_rate=0.001,
    lstm_layer_sizes=(100,)*3,
    dropout=(0,)*3,
)

f.set_estimator('prophet')
f.manual_forecast()

auto_arima(f)

# stack previously evaluated models
f.add_signals(['lstm','prophet','arima'])
f.set_estimator('catboost')
f.manual_forecast()
  1. Multivariate modeling and multivariate pipelines:
from scalecast.MVForecaster import MVForecaster
from scalecast.Pipeline import MVPipeline
from scalecast.util import find_optimal_transformation, backtest_metrics
from scalecast import GridGenerator

GridGenerator.get_mv_grids()

def mvforecaster(mvf):
    models = ['ridge','lasso','xgboost','lightgbm','knn']
    mvf.tune_test_forecast(
        models,
        limit_grid_size = .2, # randomized grid search on 20% of original grid sizes
        cross_validate = True, # cross validate? if False, using a seperate validation set that the user can specify
        rolling = True, # rolling time series cross validation?
        k = 3, # how many folds?
    )

mvf = MVForecaster(f1,f2,f3) # can take N Forecaster objects

transformer1, reverter1 = find_optimal_transformation(f1)
transformer2, reverter2 = find_optimal_transformation(f2)
transformer3, reverter3 = find_optimal_transformation(f3)

pipeline = MVPipeline(
    steps = [
        ('Transform',[transformer1,transformer2,transformer3]),
        ('Forecast',mvforecaster),
        ('Revert',[reverter1,reverter2,reverter3])
    ]
)

f1, f2, f3 = pipeline.fit_predict(f1, f2, f3)
backtest_results = pipeline.backtest(f1, f2, f3)
metrics = backtest_metrics(backtest_results)
  1. Transfer Learning (new with 0.19.0): Train a model in one Forecaster object and use that model to make predictions on the data in a separate Forecaster object.
f = Forecaster(...)
f.auto_Xvar_select()
f.set_estimator('xgboost')
f.cross_validate()
f.auto_forecast()

f_new = Forecaster(...) # different series than f
f_new = infer_apply_Xvar_selection(infer_from=f,apply_to=f_new)
f_new.transfer_predict(transfer_from=f,model='xgboost') # transfers the xgboost model from f to f_new

Installation

  • Only the base package is needed to get started:
    • pip install --upgrade scalecast
  • Optional add-ons:
    • pip install tensorflow (for RNN/LSTM on Windows) or pip install tensorflow-macos (for MAC/M1)
    • pip install darts
    • pip install prophet
    • pip install greykite (for the silverkite model)
    • pip install kats (changepoint detection)
    • pip install pmdarima (auto arima)
    • pip install tqdm (progress bar for notebook)
    • pip install ipython (widgets for notebook)
    • pip install ipywidgets (widgets for notebook)
    • jupyter nbextension enable --py widgetsnbextension (widgets for notebook)
    • jupyter labextension install @jupyter-widgets/jupyterlab-manager (widgets for Lab)

Papers that use scalecast

Udemy Course

Scalecast: Machine Learning & Deep Learning

Blog posts and notebooks

Forecasting with Different Model Types

Transforming and Reverting

Confidence Intervals

Dynamic Validation

Model Input Selection

Scaled Forecasting on Many Series

Transfer Learning

Anomaly Detection

Contributing

How to cite scalecast

@misc{scalecast,
  title = {{scalecast}},
  author = {Michael Keith},
  year = {2024},
  version = {<your version>},
  url = {https://scalecast.readthedocs.io/en/latest/},
}