Hybrid approach for time series forecasting based on a penalty p-spline and evolutionary optimization

Abstract

In this work, a hybrid-forecasting model is proposed. The model includes a recursive penalty P-spline with parameters adaptation based on evolutionary optimization algorithms. In short-term forecasting, especially in real-time systems, the urgent task is to increase the forecast speed without compromising its quality. High forecasting speed has been achieved by an economical computational scheme of a recurrent P-spline with a shallow depth of prehistory. When combined with the adaptation of some parameters of the P-spline, such an approach allows you to control the forecast accuracy.

Online process automatic monitoring technology of Internet of Things card life cycle based on time series optimization

“…Irregular fluctuations are usually mixed in the time series, resulting in a wave-like or shock-like change in the time series. A sequence containing only random fluctuations is also called a stationary sequence [6].…”

Penalized hyperbolic-polynomial splines

“…Differences are extremely easy to compute and their generalization to higher orders is straightforward. P-splines are known to be effective and appreciated in several applications (see, for example, the recent paper [3]). A comprehensive description of P-splines is given in [4].…”