Should I use Prophet or ARIMA for time series forecasting?

Prophet generally produces better results with less effort for business series with strong seasonality, trend changes, and holiday effects, while ARIMA (via statsmodels) excels when you need statistical rigor, have shorter series under 100 observations, or require interpretable parameters for regulatory documentation. A practical approach is to fit Prophet first as a baseline, then benchmark against ARIMA/SARIMA and pick ARIMA if it comes within 5% and interpretability matters.

Can sktime handle multivariate time series?

Yes, through several mechanisms: its VAR (Vector Autoregression) implementation handles multivariate statistical forecasting directly, and reduction strategies can use any scikit-learn regressor that supports multi-output prediction. For more complex multivariate relationships sktime interfaces with deep learning libraries, though Darts provides a more native multivariate deep learning experience.

What is the best Python library for deep learning time series forecasting?

Darts is the dedicated deep learning option, offering models like N-BEATS, N-HiTS, Temporal Fusion Transformers (TFT), and DeepAR with GPU acceleration through a PyTorch backend. Prophet is not a deep learning library (it uses Bayesian curve fitting), so Darts is the choice when data has irregular patterns, multiple interacting series, or long-range dependencies.

How do I prevent data leakage in time series forecasting?

Follow three rules: never use future information to compute features or predictions at time T, always split temporally so training data precedes validation data chronologically, and embed feature selection or hyperparameter tuning inside the cross-validation loop rather than running it on the full dataset first. sktime's ForecastingGridSearchCV and Darts' backtesting enforce these protections automatically, but custom code must handle them manually.

Which Python library is best for real-time time series forecasting?

Prophet is unsuitable for real-time use because it refits from scratch with no incremental update mechanism. statsmodels ARIMA supports apply for updating fitted models with new observations, making it viable for moderate-frequency updates, while for true streaming you should consider dedicated online-learning libraries like River, or deploy Darts/Prophet models with scheduled batch retraining.

Python中的时间序列分析：使用Prophet、sktime、ARIMA和Darts的完整工具包

Master Python time series analysis with Prophet, sktime, statsmodels ARIMA, and Darts. Compare tools, build forecasting pipelines, and avoid common pitfalls.

MIT
更新于 2026-05-18

Recommended Infrastructure #

To run any of the tools above reliably 24/7, infrastructure matters:

DigitalOcean — $200 free credit, 14+ global regions, one-click droplets for AI/dev workloads.
HTStack — Hong Kong VPS with low latency for mainland China access. This is the same IDC hosting dibi8.com — production-proven.

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References & Sources #

Prophet
sktime
statsmodels
Darts
tsfresh
River
scikit-learn

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