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Time Series Is Everywhere—Here’s How to Actually Forecast It

  • 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.

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