Original Contribution: Forecasting the behavior of multivariate time series using neural networks

By Kanad Chakraborty, K. Mehrotra, C. Mohan, Sanjay Ranka. 1992

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Zhongyang Han
2. K. Sudheer
3. Jun Zhao
4. Henry Leung
5. King Fai Ma
6. K. Ramasastri
7. Wei Wang
8. Zhan-Li Sun

Citations in the corpus

• A Review of Deep Learning Models for Time Series Prediction (2019, methodology): “They have been also used for nonlinear prediction in multivariate domain [52] by combining all inputs in the input layer.”
• Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications (2000, background): “The main reason for this is that ANNs belong to the class of data driven approaches, whereas conventional statistical methods are model driven (Chakraborty et al., 1992).”
• Prediction of Soil Organic Carbon across Different Land‐use Patterns (2005, methodology): “The number of training sam- whereas the statistical methods are based on model driven procedures (Chakraborty et al., 1992).”
• A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models (2002, background): “The main reason for this is that ANNs belong to the class of data-driven approaches, whereas the conventional statistical methods are model driven (Chakraborty et al., 1992).”
• Short-term load forecasting based on multivariate time series prediction and weighted neural network with random weights and kernels (2018, background): “As is known, multivariate time series often contain more dynamic information of the underlying dynamic system [15] than the univariate time series.”
• Practical options for selecting data-driven or physics-based prognostics algorithms with reviews (2015, background): “[21] concluded that it was unclear how many past values should be used as inputs.”
• Sales forecasting using extreme learning machine with applications in fashion retailing (2008, methodology): “[3] presented an ANN approach based on multivariate time-series analysis, which can accurately predict the flour prices in three cities in USA.”