Stock price prediction has been an important research theme both academically and
practically. Various methods to predict stock prices have been studied until now. The feature …

We propose a time-series gradient boosting tree for a dataset with time-series and cross-
sectional attributes. Our time-series gradient boosting tree has weak learners with time …

Research on stock market prediction has been actively conducted over time. Pertaining to
investment, stock prices and trading volume are important indicators. While extensive …

Many studies have been undertaken with machine learning techniques to predict stock
returns in terms of time-series prediction. However, from the viewpoint of the cross-sectional …

Forecasting demand for trends in their introduction phase plays an important role in strategic
planning for product manufacturers. Lack of sufficient historical data in the early stages of a …

In this study, we tackle multivariate currencies return prediction for the major 5 currencies
against the US dollar with a deep learning based multi-factor model. There are two …

Stock return prediction is one of the most important themes for investors. Until now, there are
many studies for the application of machine learning methods to predict stock returns in the …

For supervised classification problems, this paper considers estimating the query's label
probability through local regression using observed covariates. Well-known nonparametric …

Dynamic Mode Decomposition (DMD) is a new method proposed in the field of fluid analysis
that expresses the dynamics of multivariate time series data by superposition of modes …

Bu çalışmanın amacı, teknik göstergeleri girdi verisi olarak kullanarak borsa endeksi hareket
yönünün tahmin edilebilir olduğunu ortaya koymak ve sınıflandırma yöntemlerinin …