Posts tagged autoregressive

Analysis of An AR(1) Model in PyMC

Consider the following AR(2) process, initialized in the infinite past: $\( y_t = \rho_0 + \rho_1 y_{t-1} + \rho_2 y_{t-2} + \epsilon_t, \)\( where \)\epsilon_t \overset{iid}{\sim} {\cal N}(0,1)\(. Suppose you'd like to learn about \)\rho\( from a a sample of observations \)Y^T = { y_0, y_1,\ldots, y_T }$.

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Forecasting with Structural AR Timeseries

Bayesian structural timeseries models are an interesting way to learn about the structure inherent in any observed timeseries data. It also gives us the ability to project forward the implied predictive distribution granting us another view on forecasting problems. We can treat the learned characteristics of the timeseries data observed to-date as informative about the structure of the unrealised future state of the same measure.

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