Glossary

Time series and forecasting

Definition

A time series is a sequence of values measured over time — monthly sales, daily temperature, energy consumption. Time-series analysis decomposes a series into trend, seasonality and a random component and forecasts future values with models such as exponential smoothing (Holt–Winters) or ARIMA (Box–Jenkins methodology).

Regression & models

Unlike ordinary regression, observations are dependent: today's value relates to yesterday's (autocorrelation). The autocorrelation functions (ACF, PACF) therefore serve to identify an ARIMA model; a series is often differenced before modelling to make it stationary.

Validate forecast quality on a holdout period using MAE, MAPE or RMSE, and always report a prediction interval — it widens with the horizon. For series driven by many explanatory influences (prices, weather, campaigns) combine time series with regression or machine learning.

In Statistica

Statistics → Advanced Linear/Nonlinear Models → Time Series/Forecasting includes ARIMA and seasonal ARIMA with ACF/PACF, exponential smoothing including Holt–Winters, seasonal decomposition (Census I/II, X-11), spectral analysis, transformations and interactive series plots with forecasts and intervals.

Related terms

Knowledgebase guides

FAQ

ARIMA or exponential smoothing?
Exponential smoothing is simpler and robust for short-term forecasts with seasonality; ARIMA is more flexible but requires stationarity and more data (usually 50+ observations).
How much data do I need for a seasonal model?
At least three to four complete seasonal cycles — for monthly data, 3–4 years.

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