Short-term prediction of Time Series based on bounding techniques

TL;DR

This paper introduces a non-parametric time series prediction method that uses a constrained optimization approach to minimize an upper bound of prediction error, balancing deterministic and stochastic assumptions for improved short-term forecasting.

Contribution

The paper proposes a novel non-parametric prediction technique based on bounding techniques and optimization, offering an alternative to classical methods with improved short-term forecast performance.

Findings

The method effectively minimizes an upper bound of prediction error.

It outperforms existing non-parametric methods in short-term forecasts.

Benchmark results demonstrate its practical applicability.

Abstract

In this paper it is reconsidered the prediction problem in time series framework by using a new non-parametric approach. Through this reconsideration, the prediction is obtained by a weighted sum of past observed data. These weights are obtained by solving a constrained linear optimization problem that minimizes an outer bound of the prediction error. The innovation is to consider both deterministic and stochastic assumptions in order to obtain the upper bound of the prediction error, a tuning parameter is used to balance these deterministic-stochastic assumptions in order to improve the predictor performance. A benchmark is included to illustrate that the proposed predictor can obtain suitable results in a prediction scheme, and can be an interesting alternative method to the classical non-parametric methods. Besides, it is shown how this model can outperform the preexisting ones in a short term forecast.