Time series data is prevalent in various domains, ranging from stock prices to health monitoring, characterized by data points with timestamps.
Traditional statistical models like ARIMA and Prophet are effective for basic trends but struggle with noisy, nonlinear, and multivariate data.
Deep learning techniques, especially LSTMs (Long Short-Term Memory networks), excel at handling time series data with long dependencies and real-world complexities.
Reinforcement learning, typically used in game AI, can also be applied to time series forecasting for making decisions like stock trading.