Short-term prediction of Time Series based on bounding techniques
This is an incremental improvement for time series forecasting, offering an alternative to classical non-parametric methods.
The paper tackles short-term time series prediction by developing a non-parametric method that uses a weighted sum of past data, with weights optimized to minimize an outer bound of prediction error. It shows the model can outperform existing methods in short-term forecasts, though no concrete numerical results are provided.
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.