Time Series Analysis¶
Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control.
Cited by¶
4 citations across 4 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Recurrence
- Recurrence requires measurable dependencies between occurrences—a lag structure, a trigger, or a causal link—an autocorrelation-grounded distinction Box and Jenkins (1970) make central to their stochastic-process framework.
This sourceHolden-Day. Foundational text introducing the Box–Jenkins ARIMA methodology: formalizes recurrence as autocorrelation structure, distinguishing genuine state-dependence from independent (white-noise) repetition.
- Recurrence requires measurable dependencies between occurrences—a lag structure, a trigger, or a causal link—an autocorrelation-grounded distinction Box and Jenkins (1970) make central to their stochastic-process framework.
- Residual Analysis
- This is the structural basis for boosting, for hierarchical regression, for ARIMA-family decompositions, and for the standard trend-seasonal-residual decomposition of time series.
This sourceEstablishes residual (forecast-error) diagnostics and the iterative identify-estimate-check modelling cycle for ARIMA-family and trend-seasonal-residual decompositions.
- This is the structural basis for boosting, for hierarchical regression, for ARIMA-family decompositions, and for the standard trend-seasonal-residual decomposition of time series.
- Stationarity
- When stationarity holds, a large toolkit (ARMA/ARIMA models, spectral analysis, classical forecasting methods, Fourier decomposition) applies directly; when it fails, these tools systematically mislead by producing underestimated confidence intervals, missed regime shifts, and inflated forecast precision
This sourceHolden-Day. Foundational text introducing the Box–Jenkins ARIMA methodology: formalizes recurrence as autocorrelation structure, distinguishing genuine state-dependence from independent (white-noise) repetition.
- When stationarity holds, a large toolkit (ARMA/ARIMA models, spectral analysis, classical forecasting methods, Fourier decomposition) applies directly; when it fails, these tools systematically mislead by producing underestimated confidence intervals, missed regime shifts, and inflated forecast precision
Mechanisms¶
- Differencing Transform
- This is the "I" — the integrated, difference-then-model step — at the heart of Box–Jenkins ARIMA modeling.
This sourceExplains that differencing nonstationary series produces ARIMA, with “I” meaning integrated.
- This is the "I" — the integrated, difference-then-model step — at the heart of Box–Jenkins ARIMA modeling.
Verification¶
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Links previously used in the corpus¶
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