Time series forecasting using neural networks

By Thomas Kolarik, Gottfried Rudorfer. 1994

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Citations in the corpus

Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, background): “…suggest that NNs are suitable to effectively model the underlying seasonality and cyclical patterns in time series due to their universal function approximation properties, i.e., their capacity to estimate linear and non-linear functions (Zaiyong Tang et al., 1991; Marseguerra et al., 1992).”
The use of artificial neural networks in the analysis and prediction of stock prices (2011, methodology): “1) Network Architecture: The network model chosen for prediction was the MLP feedforward for its efficiency in the time series prediction process, as well as being the model chosen by [3], [18], [19], [20] and [21] for their studies, where the results obtained were satisfactory.”
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017, background): “…suggest that NNs are suitable to effectively model the underlying seasonality and cyclical patterns in time series due to their universal function approximation properties, i.e., their capacity to estimate linear and non-linear functions (Zaiyong Tang et al., 1991; Marseguerra et al., 1992).”
Prediction (2019, methodology): “(1) Preprocess the data (2) Identify the model to use (3) Estimate parameters using a first data set (4) Validate the model using a second data set (5) Predict the time series”
A fusion model of HMM, ANN and GA for stock market forecasting (2007, methodology): “However, with ARIMA models, problems arise when the variance in the time series increases or when nonlinear processes exist in the time series (Kolarik & Rudorfer, 1994).”
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.”
Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction (2011, background): “…performance of NNs, from publications on single benchmark time series such 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,…”