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Human-inspired hyperparameter optimization for long-horizon forecasting of freshwater and desalination per-capita dynamics. [PDF]
Sci RepAlhammad SM +3 more
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Forecasting Under-5 Mortality Rate in Somalia to 2030: a comparative analysis of univariate and multivariate ARIMAX models. [PDF]
Front Public HealthSeiman SMK +6 more
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Evaluation of respiratory disease hospitalisation forecasts using synthetic outbreak data. [PDF]
Commun Med (Lond)Béchade G, Lundh T, Gerlee P.
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ANFISunfoldedintime for multivariate time series forecasting
Neurocomputing, 2004This paper proposes a temporal neuro-fuzzy system named ANFIS_unfolded_in_time which is designed to provide an environment that keeps temporal relationships between the variables and to forecast the future behavior of data by using fuzzy rules. It is a modification of ANFIS neuro-fuzzy model.
N. Arzu Sisman-Yilmaz +2 more
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A Robust Approach for Multivariate Time Series Forecasting
Proceedings of the Eighth International Symposium on Information and Communication Technology, 2017Time series forecasting is often confronted with multivariate data, but few model is available in this situation. Besides, data distortion aggravates the difficulty to predict multivariate time series. To tackle such problems, we propose an approach based on convolutional neural network with a feature extraction layer added before convolution layer to ...
Ning Pang +3 more
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Deep Learning and Metaheuristic for Multivariate Time-Series Forecasting
2023Time series forecasting is a widely used statistical technique that use past data to predict future values of variables. Its applications span across various fields, including finance, economics, and marketing. Multivariate time series forecasting, which involves two or more variables, is more complex than univariate time series forecasting and to ...
Zito F., Cutello V., Pavone M.
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A deep multivariate time series multistep forecasting network
Applied Intelligence, 2021Due to that multivariate time series, multistep forecasting technology has a guiding role in many fields, such as electricity consumption, traffic flow detection, and stock price prediction, many approaches have been proposed, seeking to realize accurate prediction based on historical data.
Chenrui Yin, Qun Dai
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A Multivariate Heuristic Model for Fuzzy Time-Series Forecasting
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2007Fuzzy time-series models have been widely applied due to their ability to handle nonlinear data directly and because no rigid assumptions for the data are needed. In addition, many such models have been shown to provide better forecasting results than their conventional counterparts.
Kun-Huang Huarng +2 more
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