Forecasting Models

Forecasting models are statistical or machine learning techniques used to predict future values, trends, or events based on historical data and patterns. They are essential in fields like finance, supply chain management, weather prediction, and business planning to make informed decisions by anticipating future outcomes. These models range from simple time-series methods like ARIMA to complex machine learning algorithms such as neural networks.

Also known as: Predictive Models, Time Series Forecasting, Forecasting Algorithms, Prediction Models, Forecast Models

Why learn Forecasting Models?

Developers should learn forecasting models when building applications that require predictive analytics, such as demand forecasting in e-commerce, stock price prediction in finance, or resource planning in operations. They are crucial for optimizing business strategies, reducing uncertainty, and automating decision-making processes in data-driven environments.

Learning Resources

• book: Forecasting: Principles and Practice
• tutorial: Time Series Forecasting with Python
• course: Forecasting Models in Machine Learning
• video: ARIMA Model Tutorial
• docs: Scikit-learn Time Series Documentation