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Time-Series & Signal terms
Forecasting, seasonality and signal processing on data that arrives in order.
ARIMA (autoregressive integrated moving average) A classical time-series forecasting model that combines a series' own past values, differencing, and past forecast errors. Autocorrelation The correlation of a time series with a lagged, delayed copy of itself, used to detect repeating patterns and dependence over time. Change-point detection Identifying the points in a time series where its underlying statistical behavior shifts abruptly rather than drifting gradually. Exponential smoothing A forecasting technique that weights recent observations more heavily than older ones, with the weight decaying exponentially with age. Moving average A calculated series that averages a fixed window of recent data points, smoothing out short-term noise to reveal a trend. Numerical weather prediction (NWP) Forecasting weather by solving the physical equations governing the atmosphere on a computer, using current observations as a start. Seasonality A regular, predictable pattern in data that repeats at a fixed calendar interval, such as daily, weekly, or annually. Signal processing Techniques for analyzing, filtering, and transforming time-varying signals, such as sensor readings or audio, to extract or clean up information. Stationarity The property of a time series whose statistical characteristics, such as mean and variance, stay constant over time.