In this chapter, we introduce the concept of Time Series Forecasting, focusing on its application in stock exchange prediction. Time series forecasting involves predicting future values based on previously observed data points, which is particularly useful in fields like finance, weather forecasting, and inventory management.

Challenges: Time series forecasting, especially for stock prices, involves complexities like noise, market volatility, and external factors. The success of the prediction model depends on the quality of the data and the model's ability to capture underlying patterns.

In summary, time series forecasting is an essential tool in predicting future stock prices and making informed decisions in financial markets. The key steps—data collection, preprocessing, model selection, training, and evaluation—form the foundation of a successful forecasting project.