Noise VS Series

 Noise VS Series features

Temporal Dependence: Series features are related to time or sequences (e.g., time series, sequential data).

Patterns and Trends: These features often contain patterns, trends, or cycles that can be leveraged for forecasting or sequential analysis.

Predictive Power: Series features are typically useful in predictive modeling, as they can inform future values or states.

Require Special Models: Time series or sequential models (e.g., ARIMA, LSTMs) are often needed to properly handle these features.