Predicting agricultural commodity prices accurately is of utmost importance due to various
factors such as perishability, seasonality, production uncertainty etc. Moreover, the …

In the existing research on time-series event prediction (TSEP) methods, most of the work is
focused on improving the algorithm for classifying subsequence sets (sets composed of …

In this paper, we introduce a novel distance measure that conforms to the definition of a semi-
distance, for quantifying the similarity between Hidden Markov Models (HMMs). This …

This study demonstrates whether analysts' sentiments toward individual stocks are useful for
stock investment strategies. This is achieved by using natural language processing to create …

In this paper, we define an innovative method for predicting the stochastic behavior of the
Bull and Bear periods of the stock market. The direct study of the prediction of possible Bull …

Obtaining higher than market returns is a difficult goal to achieve, especially in times of
turbulence such as the COVID-19 crisis, which tested the resilience of many models and …

This paper presents a method for predicting stock returns using principal component
analysis (PCA) and the hidden Markov model (HMM) and tests the results of trading stocks …

Three interrelated heuristic techniques for setting the parameters of hidden Markov models
for implementation in pattern recognition algorithms of stochastic processes recorded in the …

In the ever-evolving landscape of financial markets, the pursuit of accurate stock price
predictions remains a formidable challenge. This study addresses the challenge of profitable …

La creciente atención dedicada al Machine Learning en el ámbito de las finanzas se ha
proyectado, como no podía ser de otro modo, en el ámbito de las criptomonedas. Sin …