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Time Series Forecasting with Python: Deploying and Managing Machine Learning Models in Production
Paperback
Publisher: Independently Published
Published: Jul 11 2025
Pages: 270
Weight: 0.80
Height: 0.57 Width: 6.00 Depth: 9.00
Language: English
Master time series forecasting and bring your machine learning models into production.
Time Series Forecasting with Python is the essential guide to building and deploying time series models with Python. Whether you're predicting stock prices, sales forecasts, or weather patterns, this book shows you how to develop robust models and get them into production, seamlessly integrating them into real-world applications.
You'll learn how to apply machine learning algorithms for time series forecasting, fine-tune models for accuracy, and take them from development to live deployment-while handling challenges like data drift and performance monitoring.
Inside, you'll learn how to:
Understand the fundamentals of time series data and forecasting
Preprocess and clean time series data for modeling
Build and evaluate forecasting models using ARIMA, Prophet, and LSTM
Apply machine learning techniques like XGBoost and Random Forest to time series data
Use Python libraries like pandas, statsmodels, and scikit-learn
Automate forecasting with pipelines and batch predictions
Deploy models to cloud platforms like AWS, Google Cloud, or Azure
Monitor model performance in production, and update models as needed
Integrate time series forecasting into real-world applications like dashboards and APIs
With hands-on examples, complete code snippets, and deployment tips, you'll be able to take your forecasting models from prototype to production and ensure they continue to perform well in a dynamic environment.
Whether you're a data scientist, software engineer, or business analyst, Time Series Forecasting with Python equips you with the tools to solve complex forecasting problems and deploy reliable models at scale.
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