Forecasting the volatility of Financial Time Series by Tree Ensembles

Ensembles for Time Series Forecasting

This paper describes a new type of ensembles that aims at improving the predictive performance of these approaches in time series forecasting. Ensembles are recognised as one of the most successful approaches to prediction tasks. Previous theoretical studies of ensembles have shown that one of the key reasons for this performance is diversity among ensemble members. Several methods exist to gen...

Using a Dynamic Artificial Neural Network for Forecasting the Volatility of a Financial Time Series*

The ability to obtain accurate volatility forecasts is an important issue for the financial analyst. In this paper, we use the DAN2 model, a multilayer perceptron and an ARCH model to predict the monthly conditional variance of stock prices. The results show that DAN2 model is more accurate for predicting in-sample and out-of-sample variance that the other considered models for the used dataset...

Nonlinear Time Series in Financial Forecasting

Ensembles of Recurrent Neural Networks for Robust Time Series Forecasting

Time series forecasting is a problem that is strongly dependent on the underlying process which generates the data sequence. Hence, finding good model fits often involves complex and time consuming tasks such as extensive data preprocessing, designing hybrid models, or heavy parameter optimization. Long Short-Term Memory (LSTM), a variant of recurrent neural networks (RNNs), provide state of th...

Overview and Comparison of Short-term Interval Models for Financial Time Series Forecasting

  In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. Many researchers have compared different time series models together in order to determine more efficien...