Time Series Forecasting#
Predicting future values of a time-ordered series.
Important
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What it is#
Time series forecasting predicts future values of a time-ordered series from its past. Unlike ordinary supervised learning — where rows are independent and can be shuffled — a time series has memory: order matters, and each observation is dependent on its neighbors.
The components#
A series decomposes into a trend (long-term drift up or down), seasonality (regular patterns at a fixed period), cycles (longer, aperiodic swings with no fixed length), and noise (the irregular residual). Modeling means separating these.
Stationarity and diagnostics#
Many methods need stationarity (constant mean and variance); non-stationary series are made modelable by differencing (removes trend / seasonality from the mean) and a log / Box-Cox transform (stabilizes variance). Choose the model after diagnostics — time, seasonal and lag plots, and the ACF / PACF — and evaluate with rolling-origin backtesting, reporting error by horizon and publishing intervals, not just points.
Theme: Signal Processing & Time Series · All terminology
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Mind map — connected ideas
Seasonal Lag · Log-Space · Seasonality · Naïve Baseline Forecast · Forecast Error · Point Forecasts
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More in Signal Processing & Time Series
ARIMA (AutoRegressive Integrated Moving Average) · Bayesian Time Series · Forecast Error · Forecasting Benchmarks · Forecasting Competitions · Log-Space · Low-pass Filtering · LSTM — Long Short-Term Memory Networks · M-Competitions (Makridakis Competitions) · Naïve Baseline Forecast · Prophet — Time Series Forecasting by Facebook (Meta) · Seasonal Lag · Seasonality · Signal Processing
See also
Source article Adapted (context, re-expressed) in our own words from: Time Series Forecasting (insightful-data-lab.com).