Maximum Visibility: A Novel Approach for Time Series Forecasting Based on Complex Network Theory

This article presents Maximum Visibility Approach (MVA), a new time series forecasting method based on the Complex Network theory. MVA initially maps time series data into a complex network using the visibility graph method. Then, based on the similarity measures between the nodes in the network, MVA calculates the one-step-ahead forecasts. MVA does not use all past terms in the forecasting process, but only the most significant observations, which are indicated as a result of the autocorrelation function. This method was applied to five different groups of data, most of them showing trend characteristics, seasonal variations and/or non-stationary behavior. We calculated error measures to evaluate the performance of MVA. The results of statistical tests and error measures revealed that MVA has a good performance compared to the accuracy obtained by the benchmarks considered in this work. In all cases, MVA surpassed other forecasting methods in Literature, which confirms that this work will contribute to the field of time series forecasting not only in the theoretical aspect, but also in practice.

Automated summary

This article introduces a new time series forecasting method called Maximum Visibility Approach (MVA), which is based on the Complex Network theory. MVA maps time series data into a complex network using the visibility graph method and calculates forecasts based on the similarity measures between nodes. The experimental results demonstrate that MVA outperforms other forecasting methods.