{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:01:08Z","timestamp":1783576868996,"version":"3.55.0"},"reference-count":13,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,4,18]]},"abstract":"<jats:p>Time series forecasting has a wide range of applications in various fields. To eliminate the need for time series data volume, a meta-learning-based few-shot time series forecasting method is proposed. This method uses a residual stack module as its backbone and connects the residuals forward and backward through a multilayer fully connected network so that the model and the meta-learning framework can be seamlessly combined. The Empirical knowledge of different time-sequence tasks is obtained through meta-training. To enable fast adaptation to new prediction tasks, a small meta-network is introduced to adaptively and dynamically generate the learning rate and weight decay coefficient of each step in the network. This method can use sequences of different data distribution characteristics for cross-task learning, and each training task only needs a small number of time series to achieve sequence prediction for the target task. The results show that compared with the two baselines, the proposed method has improved performance on 67.07% and 58.53% of the evaluated tasks. Thus, this method can effectively alleviate the problems caused by insufficient data during training and has broad application prospects in the field of time series.<\/jats:p>","DOI":"10.3233\/jifs-233520","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T11:13:27Z","timestamp":1709291607000},"page":"8903-8916","source":"Crossref","is-referenced-by-count":1,"title":["Few-shot time series forecasting in a meta-learning framework"],"prefix":"10.1177","volume":"46","author":[{"given":"Ping","family":"Ma","sequence":"first","affiliation":[{"name":"School of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengwei","family":"Ni","sequence":"additional","affiliation":[{"name":"School of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-233520_ref1","doi-asserted-by":"crossref","first-page":"107","DOI":"10.3390\/forecast1010008","article-title":"Fast univariate time series prediction of solarpower for real-time control of energy storage system","volume":"1","author":"Mostafa Majidpour","year":"2018","journal-title":"Forecasting"},{"issue":"4","key":"10.3233\/JIFS-233520_ref2","doi-asserted-by":"crossref","first-page":"2393","DOI":"10.3390\/su13042393","article-title":"Prospective methodologies in hybrid renewable energy systemsfor energy prediction using artificial neural networks","volume":"13","author":"Md Mijanur Rahman","year":"2021","journal-title":"Sustainability"},{"issue":"3","key":"10.3233\/JIFS-233520_ref3","doi-asserted-by":"crossref","first-page":"158","DOI":"10.4258\/hir.2010.16.3.158","article-title":"Prediction of dailypatient numbers for a regional emergency medical center using timeseries analysis","volume":"16","author":"Hye Jin Kam,","year":"2010","journal-title":"Healthcare Informatics Research"},{"issue":"1","key":"10.3233\/JIFS-233520_ref5","doi-asserted-by":"crossref","first-page":"325","DOI":"10.3233\/JIFS-212228","article-title":"Meta-learning for few-shot time series forecasting","volume":"43","author":"Feng Xiao","year":"2022","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.3233\/JIFS-233520_ref6","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.eswa.2017.02.044","article-title":"A feature weighted support vectormachine and k-nearest neighbor algorithm for stock market indicesprediction","volume":"80","author":"Yingjun Chen","year":"2017","journal-title":"Expert Systems with Applications"},{"issue":"7","key":"10.3233\/JIFS-233520_ref9","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review ofrecurrent neural networks: Lstm cells and network architectures","volume":"31","author":"Yong Yu","year":"2019","journal-title":"Neural Computation"},{"issue":"2","key":"10.3233\/JIFS-233520_ref11","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.1080\/23249935.2019.1637966","article-title":"Short-term traffic flow prediction based on spatiotemporal analysisand cnn deep learning","volume":"15","author":"Weibin Zhang","year":"2019","journal-title":"Transportmetrica A: Transport Science"},{"key":"10.3233\/JIFS-233520_ref12","doi-asserted-by":"crossref","unstructured":"Maryam Imani , , Electrical load-temperature cnn for residential loadforecasting, Energy, 227 (2021), 120480.","DOI":"10.1016\/j.energy.2021.120480"},{"key":"10.3233\/JIFS-233520_ref13","unstructured":"Shiyang Li , Xiaoyong Jin , Yao Xuan , Xiyou Zhou , Wenhu Chen , Yu-XiangWang , Xifeng Yan , Enhancing the locality and breaking the memorybottleneck of transformer on time series forecasting, Advancesin Neural Information Processing Systems 32 (2019)."},{"issue":"1","key":"10.3233\/JIFS-233520_ref18","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/s41597-019-0103-9","article-title":"Multitask learning and benchmarking with clinicaltime series data","volume":"6","author":"Hrayr Harutyunyan","year":"2019","journal-title":"Scientific Data"},{"key":"10.3233\/JIFS-233520_ref20","doi-asserted-by":"crossref","first-page":"9242","DOI":"10.1609\/aaai.v35i10.17115","article-title":"Meta-learning framework with applications to zero-shottime-series forecasting","volume":"35","author":"Boris N. 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