Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Modern Time Series Forecasting with Python: Exploring statistical models, machine learning, and deep learning for cutting-edge time series forecasting

Modern Time Series Forecasting with Python: Exploring statistical models, machine learning, and deep learning for cutting-edge time series forecasting

Rapaka, Ravindra

Paperback

General ComputersProgramming

ISBN10: 9365893623
ISBN13: 9789365893625
Publisher: Bpb Publications
Published: Mar 9 2026
Pages: 446
Weight: 1.68
Height: 0.90 Width: 7.50 Depth: 9.25
Language: English

Time series forecasting is driving decision-making in everything from financial markets to supply chain logistics. This book provides a hands-on roadmap to mastering this technology, bridging the gap between classical statistical rigor and cutting-edge artificial intelligence.

Understand time series fundamentals by exploring decomposition, stationarity, and ACF/PACF analysis before mastering preprocessing and feature engineering. You will build foundational ARIMA, SARIMA, and Holt-Winters' models before pivoting to machine learning with XGBoost and Scikit-learn. The journey accelerates into deep learning, designing RNNs, LSTMs, and hybrid CNN-LSTM architectures for univariate and multivariate forecasting. After exploring advanced VAR and VECM models, you will implement walk-forward validation and professional error metrics. The final sections cover scalability and MLOps, teaching you to handle big data with Dask and deploy production-ready models via FastAPI and Apache Kafka.

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