Designing a neural network for forecasting financial and economic time series

By Iebeling Kaastra, Milton S. Boyd. 1996

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Citations in the corpus

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks (2017, methodology): “Neural networks in forecasting have been typically applied to individual time series, i.e. a different model is ﬁtted to each time series independently [14, 8, 6].”
Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks (2015, methodology): “Traditionally neural networks have been used to model stock prices as time series for the forecasting purpose, such as in (Kaastra and Boyd, 1991; Adya and Collopy, 1991; Chan et al., 2000; Skabar and Cloete, 2002; Zhu et al., 2008).”
Using machine learning for medium frequency derivative portfolio trading (2015, methodology): “Most prior work in this area concentrates on using popular ML techniques like SVM [7], [12], [9] and neural networks [8], [3], [13] coupled with rigorously designed features, and the general area of focus is financial time series forecasting.”
Application of several optimization techniques for estimating TBM advance rate in granitic rocks (2019, background): “Several investigators introduced equations for determination ofNh (Hecht-Nielsen,1987; Ripley, 1993; Masters, 1994; Paola, 1994; Wang, 1994; Kaastra and Boyd, 1996; Kanellopoulas and Wilkinson, 1997).”
Evaluation of bidirectional LSTM for short-and long-term stock market prediction (2018, background): “As a result, it is difficult to predict or forecast the stock market behavior without more robust and highly nonlinear modeling techniques [9], [10].”
Deep Learning for Time-Series Analysis (2017, unknown): “Still in the 90’s, [29] delineated eight steps on “designing a neural network forecast model using economic time series data”.”
Research on financial early warning of mining listed companies based on BP neural network model (2021, unknown): “It can simulate the information transmission mode of human brain neurons, and make nonlinear transformation and regression processing for complex information variables, and obtain the operation results with high fitting degree (Kaastra and Boyd, 1996).”
Predicting the Price of Bitcoin Using Machine Learning (2018, background): “Prediction of mature financial markets such as the stock market has been researched at length [3], [4].”
Data‐driven projection method in fluid simulation (2016, background): “Artiﬁcial neural networks have played an important role in the ﬁelds of face recognition [17], ﬁnancial forecast [18], image classiﬁcation [19], and so on.”