A Fast Algorithm for Network Forecasting Time Series

Liu, Fan; Deng, Yong

doi:10.1109/access.2019.2926986

Cited by 39 publications

(23 citation statements)

References 87 publications

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“…Let S i,MN = max(Sim i ), that is, node M is the most similar node to N in G i , and the corresponding time node in different time-frequency spaces is (t M , y i M ). According to previous studies, the initial forecast value obtained by transforming a network into a time series satisfies [22], [90].…”

Section: B Transformation and Link Predictionmentioning

confidence: 99%

A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation

Liu

Xiao

Lin

et al. 2020

IEEE Trans. Fuzzy Syst.

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Forecasting time series is an emerging topic in operational research. Existing time series models have limited prediction accuracy when faced with the characteristics of nonlinearity and nonstationarity in complex situations related to energy and finance. To enhance overall prediction capabilities and improve forecasting accuracy, we propose a fuzzy interval time series forecasting model on the basis of network-based multiple time-frequency spaces and the induced ordered weighted averaging aggregation (IOWA) operation. Specifically, a time series signal is decomposed into ensemble empirical modes and then reconstructed as various time-frequency spaces, which are transformed into visibility graphs. Then, forecasting intervals in different spaces can be collected after the local random walker link prediction model is adopted. Furthermore, a rule-based representation value function inspired by Yager's golden rule approach is defined, and an appropriate representation value is calculated. Finally, after IOWA is used to aggregate the forecasting outcomes in different time-frequency spaces, the final forecast value can be obtained from the fuzzy forecasting interval. Considering that energy issues are of widespread interest in nature and the social economy, two cases, based on a hydrological time series from the Biliuhe River in China and two wellknown sets of financial time series data, TAIEX and HSI, are studied to test the performance of the proposed approach in comparison with existing models. Our results show that the proposed approach can achieve better performance than well-developed models.

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Section: B Transformation and Link Predictionmentioning

confidence: 99%

A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation

Liu

Xiao

Lin

et al. 2020

IEEE Trans. Fuzzy Syst.

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show abstract

“…In the associated graph, every node corresponds to the series data in the same order. Two nodes are connected if they are visible to each other, in other words, if there is a line that links the series data, provided that this ''visibility line'' does not intersect any intermediate data height [55], [56].…”

Section: B Visibility Graphmentioning

confidence: 99%

An Efficient Network Method for Time Series Forecasting Based on the DC Algorithm and Visibility Relation

Zhao

Mo²,

Deng

2020

IEEE Access

Self Cite

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Recently time series prediction based on network analysis has become a hot research topic. However, how to more accurately forecast time series with good efficiency is still an open question. To address this issue, we propose an efficient time series forecasting method based on the DC algorithm and visibility relations on the vertexes set. Firstly, the time series is mapped into the network by the DC algorithm, which is a more efficient approach to generate the visibility graph. Then, we use the variation trends (slope) of those nodes that have visibility relation with the last node to get the preliminary predictive values. Afterward, the value of the last node is adopted to obtain the revised predictive values, which are assigned different weights according to node degree and time distance to get the final weighted result. To better demonstrate the prediction performance and applicability of the proposed method, the proposed method is applied to different time series data sets. The empirical results show that the proposed method could provide a higher level of forecasting accuracy than many methods with relatively lower time complexity. INDEX TERMS Complex network, time series forecasting, node degree, visibility graph, variation trend.

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“…CCI data is a time series, and there are many forecasting methods for time series. Time series forecasting methods include statistical methods, fuzzy forecasting methods [1,2,3], complex network methods [4,5,6], evidence theory methods [7], machine learning methods [8,9], and so on.…”

Section: Introductionmentioning

confidence: 99%

Construction Cost Index Forecasting: A Multi-feature Fusion Approach

Zhan¹,

He²,

Xiao³

2021

Preprint

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The construction cost index is an important indicator in the construction industry. Predicting CCI has great practical significance. This paper combines information fusion with machine learning, and proposes a Multifeature Fusion framework for time series forecasting. MFF uses a sliding window algorithm and proposes a function sequence to convert the time sequence into a feature sequence for information fusion. MFF replaces the traditional information method with machine learning to achieve information fusion, which greatly improves the CCI prediction effect. MFF is of great significance to CCI and time series forecasting.

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A Fast Algorithm for Network Forecasting Time Series

Cited by 39 publications

References 87 publications

A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation

A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation

An Efficient Network Method for Time Series Forecasting Based on the DC Algorithm and Visibility Relation

Construction Cost Index Forecasting: A Multi-feature Fusion Approach

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