# A novel weighted approach for time series forecasting based on visibility graph

arXiv: https://arxiv.org/abs/2103.13870 · HTML: https://mlsanity.com/paper/2103.13870/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 104 references that are in the map.
- First posted on arXiv 14 Mar 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Conditional Time Series Forecasting with Convolutional Neural Networks](https://mlsanity.com/paper/1703.04691/) (2017)
- [Bayesian Temporal Factorization for Multidimensional Time Series Prediction](https://mlsanity.com/paper/1910.06366/) (2019)
- [Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation](https://mlsanity.com/paper/2008.02663/) (2020)
- [Global Models for Time Series Forecasting: A Simulation Study](https://mlsanity.com/paper/2012.12485/) (2020)
- [Explainable boosted linear regression for time series forecasting](https://mlsanity.com/paper/2009.09110/) (2020)
- [Fusion of complex networks and randomized neural networks for texture analysis](https://mlsanity.com/paper/1806.09170/) (2018)
- [Complex-valued Iris Recognition Network](https://mlsanity.com/paper/2011.11198/) (2020)
- [Identifying influential nodes in complex networks: Effective distance gravity model](https://mlsanity.com/paper/2011.06348/) (2020)
- [Identification of influencers in complex networks by local information dimensionality](https://mlsanity.com/paper/1908.11298/) (2019)
- [Vital Spreaders Identification in Complex Networks with Multi-Local Dimension](https://mlsanity.com/paper/1909.13646/) (2019)
- [A betweenness structure entropy of complex networks](https://mlsanity.com/paper/1407.0097/) (2014)
- [Efficient On-line Computation of Visibility Graphs](https://mlsanity.com/paper/1905.03204/) (2019)
- [Visibility graphs for image processing](https://mlsanity.com/paper/1804.07125/) (2018)
- [Passive network evolution promotes group welfare in complex networks](https://mlsanity.com/paper/1910.04446/) (2019)

## Built on by (most cited first, 2 of 2)

- [TimeFormer: Transformer with Attention Modulation Empowered by Temporal Characteristics for Time Series Forecasting](https://mlsanity.com/paper/2510.06680/) (2025)
- [Construction Cost Index Forecasting: A Multi-feature Fusion Approach](https://mlsanity.com/paper/2108.10155/) (2021)

## Abstract (arXiv)

Time series has attracted a lot of attention in many fields today. Time series forecasting algorithm based on complex network analysis is a research hotspot. How to use time series information to achieve more accurate forecasting is a problem. To solve this problem, this paper proposes a weighted network forecasting method to improve the forecasting accuracy. Firstly, the time series will be transformed into a complex network, and the similarity between nodes will be found. Then, the similarity will be used as a weight to make weighted forecasting on the predicted values produced by different nodes. Compared with the previous method, the proposed method is more accurate. In order to verify the effect of the proposed method, the experimental part is tested on M1, M3 datasets and Construction Cost Index (CCI) dataset, which shows that the proposed method has more accurate forecasting performance.