Answered: what is structural time series…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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what is structural time series forecasting? state the advantage and disadvantage of using it.

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Many time series display common characteristics, such as a general upwards or downwards trend; repeating, and potentially nested, patterns; or sudden spikes or drops. Structural approaches to time series address these features explicitly by representing an observed time series as a combination of components.

There are two broad approaches to structural time series.

In the first, a structural time series is treated as a state space model. In this approach, the values of the time series we observe are generated by latent space dynamics. This encompasses an enormously broad and flexible class of models, including the likes of ARIMA and the Kalman filter. Popular open source packages like bsts (Bayesian Structural Time Series, in R) and the TensorFlow Probability sts module support the state space model formulation of structural time series.
In the second approach, structural time series are generalized additive models (GAMs), where the time series is decomposed into functions that add together (each of which may itself be the product or sum of multiple components) to reproduce the original series. This approach casts the time series problem as curve fitting, which does incur some trade-offs. On the upside, it renders the model interpretable, easy to debug, and able to handle missing and irregularly spaced observations. On the other hand, we are likely to lose some accuracy, as compared to autoregressive approaches that consider the previous few (or many) data points for each next prediction. Just as with state space models, GAMs may be treated in a Bayesian fashion, affording us a posterior distribution of forecasts that capture uncertainty.

Both approaches have their place. In this report, we’ll discuss the latter. The GAM approach is not universally referred to as a structural time series (which more often refers to state space models), but here we take a broader view of the term: a structural time series model is any model that seeks to decompose time series data into constituent components—which generalized additive models can be applied to do.