SCALECAST 0.14.8

The practitioner's time series forecasting library

🌄 Scalecast: The practitioner's time series forecasting library

About

Scalecast is a light-weight modeling procedure, wrapper, and results container meant for those who are looking for the fastest way possible to apply, tune, and validate many different model classes for forecasting applications. In the Data Science industry, it is often asked of practitioners to deliver predictions and ranges of predictions for several lines of businesses or data slices, 100s or even 1000s. In such situations, it is common to see a simple linear regression or some other quick procedure applied to all lines due to the complexity of the task. This works well enough for people who need to deliver something, but more can be achieved.

The scalecast package was designed to address this situation and offer advanced machine learning models and experiments that can be applied, optimized, and validated quickly. Unlike many libraries, the predictions produced by scalecast are always dynamic by default, not averages of one-step forecasts, so you don't run into the situation where the estimator looks great on the test-set but can't generalize to real data. What you see is what you get, with no attempt to oversell results. If you download a library that looks like it's able to predict the COVID pandemic in your test-set, you probably have a one-step forecast happening under-the-hood. You can't predict the unpredictable, and you won't see such things with scalecast.

from scalecast.Forecaster import Forecaster
from scalecast.SeriesTransformer import SeriesTransformer
from scalecast import GridGenerator
import matplotlib.pyplot as plt
import pandas_datareader as pdr

models = (
  'elasticnet',
  'lightgbm',
  'mlp',
)

GridGenerator.get_example_grids()

df = pdr.get_data_fred(
  'HOUSTNSA',
  start='1959-01-01',
  end='2022-08-01'
)
f = Forecaster(
  y=df['HOUSTNSA'],
  current_dates=df.index,
  future_dates=24,
)

transformer = SeriesTransformer(f)
f = transformer.LogTransform()
f = transformer.DiffTransform(1)

# validation splits
f.set_test_length(0.2)
f.set_validation_length(24)
# prepare series for forecast
f.add_covid19_regressor()
f.auto_Xvar_select(cross_validate=True)
f.determine_best_series_length()

f.tune_test_forecast(models)

f = transformer.DiffRevert(1)
f = transformer.LogRevert()

f.reeval_cis() # expanding cis based on all model results
f.plot(ci=True)
plt.show()

results = f.export(
  ['model_summaries','lvl_fcsts']
)

The library provides the Forecaster (for one series) and MVForecaster (for multiple series) wrappers around the following estimators:

• Scikit-Learn
  • ElasticNet
  • Gradient Boosted Trees
  • k-Nearest Neighbors
  • Lasso
  • Multi-level Perceptron
  • Multiple Linear Regression
  • Random Forest
  • Ridge
  • Stochastic Gradient Descent
  • Support Vector Machine
  • Any other sklearn regression model or regression model that uses an sklearn interface can be ported
• XGBoost
• LightGBM

The Forecaster object only can use:

• StatsModels
  • ARIMA
  • Holt-Winters Exponential Smoothing
• Darts
  • Four Theta
• Keras TensorFlow Cells
  • LSTM
  • SimpleRNN
  • Dense
• Facebook Prophet
• LinkedIn Greykite
  • silverkite
• Native Combo model

The MVForecaster object only can use:

• StatsModels
  • VECM

Want more models? Open a feature request!

The library interfaces nicely with interactive notebook applications.

In addition, scalecast offers:

• Model Validation
  • Grid search on validation data
  • Grid search using time series cross validation
  • Backtest
• Probabilistic Forecasting
• Model input analysis
  • Feature importance scoring
    • SHAP
    • Permutated feature scoring with ELI5
  • Summary stats for descriptive models
  • Auto Feature Selection
  • Feature reduction
• Anomaly detection
• Changepoint detection
• Series transformation/revert functions

Installation

• Only the base package is needed to get started:
  pip install --upgrade scalecast
• Optional add-ons:
  pip install darts
pip install fbprophet (see here to resolve a common installation issue if using Anaconda)
  pip install greykite
pip install shap (SHAP feature importance)
  pip install kats (for changepoint detection)
  pip install pmdarima (auto arima)
  pip install tqdm (progress bar with notebook)
  pip install ipython (widgets with notebook)
  pip install ipywidgets (widgets with notebook)
  jupyter nbextension enable --py widgetsnbextension (widgets with notebook)
  jupyter labextension install @jupyter-widgets/jupyterlab-manager (widgets with Lab)

Links

Official Docs

• Read the Docs
• Introductory Example
• Examples Repository
• Change Log

Forecasting with Different Model Types

• Sklearn Univariate
  • Expand your Time Series Arsenal with These Models
  • Notebook
• Sklearn Multivariate
  • Multiple Series? Forecast Them together with any Sklearn Model
  • Notebook
• RNN
  • Exploring the LSTM Neural Network Model for Time Series
  • LSTM Notebook
  • RNN Notebook
• ARIMA
  • Forecast with ARIMA in Python More Easily with Scalecast
  • Notebook
• Theta
  • Easily Employ A Theta Model For Time Series
  • Notebook
• VECM
  • Employ a VECM to predict FANG Stocks with an ML Framework
  • Notebook
• Other Notebooks
  • Prophet
  • Combo
  • Holt-Winters Exponential Smoothing
  • Silverkite

The importance of dynamic validation

• How Not to be Fooled by Time Series Models
• Model Validation Techniques for Time Series
• Notebook

Model Input Selection

• Variable Reduction Techniques for Time Series
• Auto Model Specification with ML Techniques for Time Series
• Notebook 1
• Notebook 2

Scaled Forecasting on Many Series

• M4
• May the Forecasts Be with You
• Notebook

Anomaly Detection

• Anomaly Detection for Time Series with Monte Carlo Simulations
• Notebook1
• Notebook2

See Contributing

• Contributing.md