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Grant support
The authors would like to thank the Spanish Ministry of Science and Innovation for the support under the projects PID2020-117954RB-C2 and TED2021-131311B-C22 and the European Regional Development Fund and Junta de Andalucia for projects PY20-00870, P18-RT-2778 and UPO-138516.
Analysis of institutional authors
Melgar-Garcia, LauraAuthorIdentifying novelties and anomalies for incremental learning in streaming time series forecasting
Publicated to:Engineering Applications Of Artificial Intelligence. 132 106326- - 2023-04-17 132(), DOI: 10.1016/j.engappai.2023.106326
Authors: Melgar-Garcia, Laura; Gutierrez-Aviles, David; Rubio-Escudero, Cristina; Troncoso, Alicia
Affiliations
Abstract
Time series data can be defined as a chronological sequence of observations on a variable of interest. A streaming time series is a time series that arrives continuously at high speed and has a data distribution that may change over time. Streaming time series data usually comes from electronic devices such as sensors and many of the applications dealing with streaming data in Industry 4.0 require real-time responses. Performing real-time forecasting offers the possibility to consider new types of patterns in the incoming streaming data, which is not possible when working with batch models. This paper presents a new approach to detect novelties and anomalies in real-time using a nearest-neighbors based forecasting algorithm. The algorithm works with an offline base model that is updated as stream data arrives following an incremental learning approach. It detects unknown patterns called novelties and anomalies. Novelties are included in the model in an online way and anomalies trigger an alarm as they present unexpected behaviors that need to be specifically analyzed. The algorithm has been tested with Spanish electricity demand data. Results show that the prediction errors obtained when the model is updated considering novelties and anomalies are lower than the errors obtained when the model is not updated. Thus, the model adjusts in real-time to the new patterns of data providing accurate errors and real-time predictions.
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Quality index
Bibliometric impact. Analysis of the contribution and dissemination channel
The work has been published in the journal Engineering Applications Of Artificial Intelligence due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position 5/181, thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering, Multidisciplinary. Notably, the journal is positioned above the 90th percentile.
From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.14. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)
This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:
- Field Citation Ratio (FCR) from Dimensions: 7.65 (source consulted: Dimensions Jul 2025)
Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-09, the following number of citations:
- WoS: 7
Impact and social visibility
Leadership analysis of institutional authors
There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (MELGAR GARCIA, LAURA) .