Publication2022
Fuzzy Time Series Forecasting Approach using LSTM Model
The record
- Type
- Publication
- Date
- 1 January 2022
- Level
- National
- Journal
- Computacion Y Sistemas
- Volume
- 26
- Issue
- 1
- Pages
- 485-492
- Published
- 1 January 2022
- DOI
- 10.13053/cys-26-1-4192
- Scopus ID
- 85130758145
- ISSN
- 14055546
- Authors
- 4
- Open access
- No
- Citations
- 9
- International co-authorship
- No
- Co-author countries
- IN
Classification
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UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Develops fuzzy long short-term memory model for time series forecasting with reduced computational complexity, outperforming competing approaches across multiple test datasets.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3.1 Research and development
Advances machine learning research methodology through novel hybrid fuzzy-LSTM approach for predictive analytics applications.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
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- 1
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Citing this record
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KIIT Deemed to be University. “Fuzzy Time Series Forecasting Approach using LSTM Model”. Kortex.