Connectionist approach to time series prediction: an empirical test

By Ramesh Sharda, Rajendra B. Patil. 1992

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Citations in the corpus

Avoiding Pitfalls in Neural Network Research (2007, background): “In addition, the mathematical property of the neural network in accurately approximating or representing various complex relationships has been established and supported by solid theoretical work [9], [15], [23], [34], [48], [59], [64]–[67], [125], [126].”
Application of support vector machines in !nancial time series forecasting (2001, methodology): “In recent years, neural networks have been successfully used for modeling ÿnancial time series [3] [4] [5].”
A comparative study of linear and nonlinear models for aggregate retail sales forecasting (2003, background): “Sharda and Patil (1992) found empirically that neural networks can model seasonality effectively and pre-deseasonalizing the data is not necessary.”
Financial time series prediction using polynomial pipelined neural networks (2008, methodology): “The use of neural network models for the prediction of financial time series as multivariate models has shown significant improvements in terms of prediction and financial metrics (Abecasis & Lapenta, 1996; Cheng, Wanger, & Lin, 1996; Sharda & Patil, 1993; Van & Robert, 1997).”
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions (2019, background): “Smyl (2017) segments the problem into two parts; developing a group of specialized RNN models and ensembling them to derive a combined prediction. That author’s methodology follows the idea that general clustering based on a standard metric for developing ensembles does not offer the best performanc”
Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction (2011, background): “…as the popular airline passenger dataset (Faraway & Chatfield, 1998; Kolarik & Rudorfer, 1994; Tang & Fishwick, 1993), to representative subsets of established benchmarks from previous forecasting competitions (Foster, Collopy , & Ungar, 1992; Hill, O'Connor, & Remus, 1996; Sharda & Patil, 1992).”
An Empirical Comparison of Machine Learning Models for Time Series Forecasting (2010, methodology): “For example Sharda and Patil [47] have compared neural networks to ARIMA on the M-competition time series data.”
Neural Networks In Business Time Series Forecasting: Benefits And Problems (2011, result): “Later, Sharda and Patil (1992) and Tang et al. (1991, 1993) found that NNs outperformed Autobox for time series with short memory and/or with noise, and produced similar forecasting results for series with long memory.”
Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition (2011, methodology): “…forecasting problem, and their performance is quite encouraging (see for example Alon, Qi, & Sadowski, 2001; Callen, Kwan, Yip, & Yuan, 1996; Hill, O'Connor, & Remus, 1996; Sharda & Patil, 1992; Terasvirta, van Dijk, & Medeiros, 2005; Zhang & Qi, 2005; and the review by Adya & Collopy, 1998).”