Time Series Forecasting with Python: Deploying and Managing Machine Learning Models in Production

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.