Moving Averages (MA) are statistical techniques that smooth time series data by calculating rolling averages over a fixed window. Two main types:

• Simple Moving Average (SMA): SMA = (x₁ + x₂ + ... + xₚ) / p. Equal weight to all p values in window.
• Exponential Moving Average (EMA): EMA = (current × multiplier) + (previous EMA × (1 − multiplier)). Recent values weighted more heavily; multiplier = 2/(period + 1).

Key Characteristics:

• Reduces noise and random fluctuations in data.
• Reveals underlying trends by smoothing.
• SMA: symmetric, stable. EMA: responsive, faster trend detection.
• Lagging indicator—reacts after markets move.
• Window size controls smoothness vs. responsiveness trade-off.

Common Uses: Technical analysis, forecasting, demand planning, quality control, signal processing.