• Data smoothing is the process of applying a mathematical procedure to a series of observations to reduce short‑term fluctuations (noise) and highlight longer‑term trends or cycles. It does not create new information; it reduces visible volatility so patterns become easier to see and interpret.
Key concepts and short definitions
– Noise: random or short‑lived variability in a data series that can obscure underlying patterns.
– Trend: a persistent directional movement in data over time (up, down, sideways).
– Seasonality: regular, recurring variation tied to calendar effects (e.g., monthly retail sales spikes).
– Outlier: a data point that differs markedly from others and may be an error or a meaningful anomaly.
– Simple moving average (SMA): the arithmetic mean of the most recent n observations; places equal weight on each of the n points.
– Exponential moving average (EMA): a weighted average that gives more weight to recent observations; computed recursively with a smoothing factor alpha.
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