sktime: Unified Framework for Time Series Machine Learning

Warnings

• gotcha sktime relies heavily on 'soft dependencies'. Many estimators (especially advanced or classical statistical models) require additional packages that are not installed by default with `pip install sktime`. Attempting to use these estimators without their dependencies will result in `ModuleNotFoundError` or similar.
  Fix: Install `sktime` with specific extras, e.g., `pip install sktime[forecasting]` or `pip install sktime[all_extras]` to ensure all necessary dependencies for your desired models are present.
• breaking sktime undergoes regular API refinements and deprecations. Features, classes, or functions marked for deprecation in one release are often removed in subsequent major/minor releases (e.g., 0.38.0, 0.39.0 introduced scheduled deprecations).
  Fix: Always check the official `sktime` changelog and release notes when upgrading versions. Pay close attention to deprecation warnings in your code and update your imports/API calls accordingly.
• gotcha sktime has specific Python version requirements (currently `>=3.10, <3.15`). Using an unsupported Python version can lead to installation issues or runtime errors, particularly with dependencies.
  Fix: Ensure your Python environment meets the `sktime`'s `requires_python` specification. Use a virtual environment to manage dependencies and Python versions.
• gotcha Compatibility with `scikit-learn` versions can be sensitive. Hotfixes are frequently released to address `scikit-learn` version updates (e.g., 0.38.1 for `scikit-learn 1.7`).
  Fix: If encountering issues after a `scikit-learn` upgrade, check `sktime`'s latest patch releases for compatibility fixes. Consider pinning `scikit-learn` to a known compatible version in production environments.

Install

• pip install sktime Basic installation
• pip install sktime[all_extras] Full installation with all optional dependencies
• pip install sktime[forecasting] Installation with forecasting-specific dependencies

Imports

• Forecaster

  from sktime.forecasting.base import BaseForecaster

  Users often mistake `model_selection` for base estimator imports. Estimators are generally in `sktime.<task>.<model_name>`.
• ThetaForecaster

  from sktime.forecasting.theta import ThetaForecaster

• temporal_train_test_split

  from sktime.forecasting.model_selection import temporal_train_test_split

• StandardScaler

  from sktime.transformations.series.scaling import StandardScaler

Quickstart

This quickstart demonstrates a basic time series forecasting workflow using `sktime`. It covers data preparation, model initialization, fitting, and prediction with the `ThetaForecaster`. Note that plotting requires the `matplotlib` library, which is an optional dependency.

import pandas as pd
from sktime.forecasting.model_selection import temporal_train_test_split
from sktime.forecasting.theta import ThetaForecaster
from sktime.utils.plotting import plot_series # requires 'matplotlib' optional dependency

# 1. Data loading and splitting
y = pd.Series([10, 12, 13, 15, 18, 20, 22, 25, 28, 30])
y.index = pd.to_datetime(pd.date_range("2020-01-01", periods=10, freq="M"))
y_train, y_test = temporal_train_test_split(y, test_size=3)

# 2. Model selection and fitting
forecaster = ThetaForecaster(sp=1)
forecaster.fit(y_train)

# 3. Prediction
fh = [1, 2, 3]  # forecast horizon for next 3 periods
y_pred = forecaster.predict(fh=fh)

print("Training data:\n", y_train)
print("Test data:\n", y_test)
print("Predictions:\n", y_pred)

# To plot, ensure matplotlib is installed (pip install matplotlib)
# plot_series(y_train, y_test, y_pred, labels=["y_train", "y_test", "y_pred"])