Boltzmann entropy series#

Boltzmann entropy series#

Connectionist approach to time series prediction: An empirical test. Learning representation by backpropagation errors. Proceedings of the Twenty-Fifth Annual Hawaii International Conference on System Sciences, 1992.ĭ.

Currency exchange rate forecasting by error backpropagation. Text mining systems for market response to news: A survey. Forecasting jummp arrivals in stock prices: new attention-based network architecture using limit order book data. Cnn-lstm neural network model for quantitative strategy analysis in stock markets. Efficient capital markets and martingales. Expert systems with applications predicting direction of stock prce index movement using artificial neural networks and support vector machines. IEEE International Conference on Acoustic, Speech and Signal Processing, 2017. Generative adversarial network-based postfilter for statistical parametric speech synthesis. Predictability of stock returns: Robustness and economic significance. MPRA Munich Personal RePEc Archive, 2020. Generative adversarial network for market hourly discrimination. Boltzmann entropy in cryptocurrencies: A statistical ensemble based approach. Advances in Neural Information Processing System, 2014.

Journal of the Air & Waste Management Association, 1995. Forecasting air quality time series using deep learning. Efficient capital markets: A review of theory and empirical work. Dividend yields and expected stock returns. A high-frequency trade execution model for supervised learning. Exploring the attention mechanism in lstm-based honk kong stock price movement prediction. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. An introductory study in time series modeling and forecasting. G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation Neural Network Price Forecasting LSTM EntropyĬ - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related TopicsĮ - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applicationsį - International Economics > F1 - Trade > F17 - Trade Forecasting and Simulation How Boltzmann Entropy Improves Prediction with LSTM In particular, we show how it is possible to increase the scope of entropy by moving from cryptocurrencies to equities and how this type of architectures highlight the link between the indicators and the information that they are able to contain. In this paper we want to demonstrate how it is possible to improve the forecast by using Boltzmann entropy like the classic financial indicators, throught neural networks.