Hierarchical Meta-Learning in Time Series Forecasting for Improved Interference-Less Machine Learning Article Swipe
David Afolabi
,
Sheng-Uei Guan
,
Jieming Ma
,
Prudence W. H. Wong
,
Xuan Zhao
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/sym9110283
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/sym9110283
The importance of an interference-less machine learning scheme in time series prediction is crucial, as an oversight can have a negative cumulative effect, especially when predicting many steps ahead of the currently available data. The on-going research on noise elimination in time series forecasting has led to a successful approach of decomposing the data sequence into component trends to identify noise-inducing information. The empirical mode decomposition method separates the time series/signal into a set of intrinsic mode functions ranging from high to low frequencies, which can be summed up to reconstruct the original data. The usual assumption that random noises are only contained in the high-frequency component has been shown not to be the case, as observed in our previous findings. The results from that experiment reveal that noise can be present in a low frequency component, and this motivates the newly-proposed algorithm. Additionally, to prevent the erosion of periodic trends and patterns within the series, we perform the learning of local and global trends separately in a hierarchical manner which succeeds in detecting and eliminating short/long term noise. The algorithm is tested on four datasets from financial market data and physical science data. The simulation results are compared with the conventional and state-of-the-art approaches for time series machine learning, such as the non-linear autoregressive neural network and the long short-term memory recurrent neural network, respectively. Statistically significant performance gains are recorded when the meta-learning algorithm for noise reduction is used in combination with these artificial neural networks. For time series data which cannot be decomposed into meaningful trends, applying the moving average method to create meta-information for guiding the learning process is still better than the traditional approach. Therefore, this new approach is applicable to the forecasting of time series with a low signal to noise ratio, with a potential to scale adequately in a multi-cluster system due to the parallelized nature of the algorithm.
Related Topics
Concepts
Computer science
Noise (video)
Time series
Series (stratigraphy)
Artificial intelligence
Artificial neural network
Machine learning
Meta learning (computer science)
Autoregressive model
Algorithm
Hilbert–Huang transform
Mode (computer interface)
Data set
Mathematics
Statistics
White noise
Economics
Image (mathematics)
Management
Paleontology
Biology
Operating system
Telecommunications
Task (project management)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/sym9110283
- https://www.mdpi.com/2073-8994/9/11/283/pdf?version=1511175105
- OA Status
- gold
- Cited By
- 20
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2770041360
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2770041360Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/sym9110283Digital Object Identifier
- Title
-
Hierarchical Meta-Learning in Time Series Forecasting for Improved Interference-Less Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-11-18Full publication date if available
- Authors
-
David Afolabi, Sheng-Uei Guan, Jieming Ma, Prudence W. H. Wong, Xuan ZhaoList of authors in order
- Landing page
-
https://doi.org/10.3390/sym9110283Publisher landing page
- PDF URL
-
https://www.mdpi.com/2073-8994/9/11/283/pdf?version=1511175105Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2073-8994/9/11/283/pdf?version=1511175105Direct OA link when available
- Concepts
-
Computer science, Noise (video), Time series, Series (stratigraphy), Artificial intelligence, Artificial neural network, Machine learning, Meta learning (computer science), Autoregressive model, Algorithm, Hilbert–Huang transform, Mode (computer interface), Data set, Mathematics, Statistics, White noise, Economics, Image (mathematics), Management, Paleontology, Biology, Operating system, Telecommunications, Task (project management)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
20Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 1, 2022: 3, 2021: 3Per-year citation counts (last 5 years)
- References (count)
-
22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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