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

Abstract

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.

Time-series forecasting through recurrent topology

“…Finally, the Canadian lynx dataset is a well-known dataset that has long been associated with time-series analysis 4 , 18 . It has been recently and independently benchmarked using a variety of forecasting techniques including neural network-based models 41 . The dataset consists of the annual Canadian lynx trapped in the Mackenzie River district of North-West Canada for the period 1821-1934 that reflect fluctuations in the size of the lynx population 4 , 18 .…”

“…In recent years, machine-learning methods, including NNET models have attracted increasingly more attention with respect to time-series forecasting. These models have been widely used and compared to various traditional time-series models as they represent an adaptable computing framework that can be used for modelling a broad range of time-series data 6 , 41 , 43 . It is therefore not surprising that NNET is becoming one of the most popular machine-learning methods for forecasting time-series data 6 , 43 .…”

Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System

“…Artificial neural networks are a type of artificial intelligence technology that mimics the human brain’s powerful ability to recognize patterns [ 39 ]. These models have been used successfully for modelling a broad range of time-series data [ 36 , 38 , 41 , 42 ]. The task of the artificial neural network is to model the underlying data-generating process during training so that valid forecasts can be made when the parameterized model is subsequently presented with new input data [ 35 ].…”

A symmetric adaptive visibility graph classification method of orthogonal signals for automatic modulation classification

“…Experimental results show that the method effectively mitigates the batch-increase problem. Moreira et al [34] designed a Maximum Visibility Approach, or MVA, method for time series forecasting and then used the similarity measure between network nodes to further predict the value at a specific moment. [35] proposed a novel framework for bearing-fault detection based on differential visibility graphs, or DVGs, by selecting the degree distribution from the graph domain transformed vibration time series as features and using a bi-directional LSTM network, or Bi-LSTM, to distinguish different faults; outstanding fault-detection accuracy was achieved.…”