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 techniques. In this paper, we propose a systematic way for generating rich features using context-free grammars. Our proposed methodology identifies potential candidates for new technical indicators that consistently improve forecasts compared to some well-known indicators. The notion of grammar families as a compact representation to generate a rich class o...
Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, comple...
The financial market is a highly complex and dynamic system that has great commercial value; thus, m...
We propose a stochastic context-free grammar model whose structure can al-ternatively be viewed as a...
This book proposes a novel approach for time-series prediction using machine learning techniques wit...
One of the most important steps when employing machine learning approaches is the feature engineerin...
One of the most important steps when employing machine learning approaches is the feature engineerin...
Natural language processing (NLP), or the pragmatic research perspective of computational linguistic...
Financial time series forecasting is undoubtedly the top choice of computational intelligence for fi...
Abstract: In this paper, the concept of a long memory system for forecasting is developed. Pattern m...
Time Series Forecasting is vital for wide range of domains such as financial market forecasting, ear...
Recent stock market studies adopting machine learning and deep learning techniques have achieved rem...
In recent years, machine learning algorithms have become increasingly popular in financial forecasti...
This thesis focuses on the field of financial forecasting. Most studies that use the financial news ...
When forecasting financial time series, incorporating relevant sentiment analysis data into the feat...
When forecasting financial time series, incorporating relevant sentiment analysis data into the feat...
Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, comple...
The financial market is a highly complex and dynamic system that has great commercial value; thus, m...
We propose a stochastic context-free grammar model whose structure can al-ternatively be viewed as a...
This book proposes a novel approach for time-series prediction using machine learning techniques wit...
One of the most important steps when employing machine learning approaches is the feature engineerin...
One of the most important steps when employing machine learning approaches is the feature engineerin...
Natural language processing (NLP), or the pragmatic research perspective of computational linguistic...
Financial time series forecasting is undoubtedly the top choice of computational intelligence for fi...
Abstract: In this paper, the concept of a long memory system for forecasting is developed. Pattern m...
Time Series Forecasting is vital for wide range of domains such as financial market forecasting, ear...
Recent stock market studies adopting machine learning and deep learning techniques have achieved rem...
In recent years, machine learning algorithms have become increasingly popular in financial forecasti...
This thesis focuses on the field of financial forecasting. Most studies that use the financial news ...
When forecasting financial time series, incorporating relevant sentiment analysis data into the feat...
When forecasting financial time series, incorporating relevant sentiment analysis data into the feat...
Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, comple...
The financial market is a highly complex and dynamic system that has great commercial value; thus, m...
We propose a stochastic context-free grammar model whose structure can al-ternatively be viewed as a...