Robustify Financial Time Series Forecasting with Bagging

Nonlinear Time Series in Financial Forecasting

Forecasting economic and financial time-series with non-linear models

In this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among non-linear forecasting models for economic and financial time series. We review theoretical and empirical issues, including predictive density, interval and point evaluation and model selection, loss functions, data-mining, and aggregation. In addition, we argue that although the evidence in fav...

Forecasting Financial Time Series with Grammar-Guided Feature Generation

The application of machine learning techniques to forecast financial time-series is not a recent development, yet it continues to attract considerable attention due to the difficulty of the problem which is compounded by the non-linear and non-stationary nature of the time-series. The choice of an appropriate set of features is crucial to improve forecasting accuracy of machine learning techniq...

Financial time series forecasting with machine learning techniques: a survey

Stock index forecasting is vital for making informed investment decisions. This paper surveys recent literature in the domain of machine learning techniques and artificial intelligence used to forecast stock market movements. The publications are categorised according to the machine learning technique used, the forecasting timeframe, the input variables used, and the evaluation techniques emplo...

Forecasting Financial Time Series with Multiple Kernel Learning

This paper introduces a forecasting procedure based on multivariate dynamic kernels to re-examine –under a non linear framework– the experimental tests reported by Welch and Goyal showing that several variables proposed in the academic literature are of no use to predict the equity premium under linear regressions. For this approach kernel functions for time series are used with multiple kernel...