An autoregressive model represents a persistent time series whose current value depends on a previous value and a random disturbance. For inflation, an AR(1) process combines lagged inflation with a long-term average, allowing high inflation to persist while gradually reverting toward typical levels. section 5.7
Autoregressive model
A time-series model in which the current value depends linearly on one or more previous values and a random error term.
How Build a Robo-Advisor with Python (From Scratch) covers it
- How deep it goes
- Estimates one-lag parameters with regression and implements simulated paths, stopping at the AR(1) case.
- Examples
- Uses CPI-U data, lagged-inflation regression, residual volatility, and simulated thirty-year wealth paths.
- What you can do after
- Build an AR(1) inflation component and insert it into a retirement Monte Carlo simulation.
- How it connects
- Connects inflation risk with mean reversion, linear regression, random shocks, and Monte Carlo simulation.
Also explained in
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Build a Text-to-Image Generator (from Scratch)A process that produces an ordered output sequence one element at a time, conditioning each element on previously generated elements and available conditioning information.
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Statistics Every Programmer NeedsAn autoregressive component models the current value of a time series as a linear function of specified previous values.
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The Art of AI Product DevelopmentAn autoregressive language model generates each token by predicting it from the preceding context.
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Introduction to Generative AI, Second EditionAutoregressive generation produces a sequence by predicting each successive element from previously generated elements and available context.
Compare how 5 books explain autoregressive model →
Related concepts
- bidirectional language modelcontrasts with · A bidirectional language model represents or predicts tokens using contextual information from both preceding and following tokens in a sequence.
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- forward passuses · A forward pass is a calculation that propagates inputs through an ordered computational or dependency structure to produce outputs or derived values.
- decodeincludes · Decoding is the process of transforming an encoded or internal representation into an interpretable or target-form output.
- arimapart of · ARIMA is a statistical time-series model that combines autoregressive terms, differencing, and moving-average terms to represent temporal dependence and generate forecasts.
This explainer was generated by Manning's knowledge graph from the text of Build a Robo-Advisor with Python (From Scratch). The section links point to the parts of the book it draws on.




