Paper: A Novel Time Series Forecasting Method Based on Fuzzy Visibility Graph
Problem
define a fuzzy visibility graph (undirected weighted graph), then give a new similarity measure of time series.
Problem: 1. some significant information of the time series, such as trend information is lost by using visibility graph. 2. the original method for constructing visibility graphs is very sensitive to noise.
Keywords
fuzzy visibility graph
What they did
they transform the time series into an undirected weighted graph, which include more edges than original VG method.
Related work
visibility graph
Methodology
1. convert time series into fuzzy visibility graphs.
the weight of the edge between each two nodes is the visibility.
2. the similarity of two time series
Time series forecasting method:
1. divide time window
X is divided into n - m + 1 subsequences with a window of length m and a step size of 1:
2. construct fuzzy visibility graphs
adjacency matrix Ai.
3. construct the difference subsequences;
the first-order difference sequence Dsi, which reflects the change trend of the subsequence Ci
4. calculate similarities between subsequences
calculate the similarities between Cn-m+1 and the front n-m subsequences.
5. calculate the predicted value at time tn+1
??
Results
the proposed method can obtain better prediction results on small-scale data sets.
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