A New Time Series Similarity Measurement Method Based on Fluctuation Features

Time series similarity measurement is one of the fundamental tasks in time series data mining, and there are many studies on time series similarity measurement methods. However, the majority of them only calculate the distance between equal-length time series, and also cannot adequately reflect the fluctuation features of time series. To solve this problem, a new time series similarity measurement method based on fluctuation features is proposed in this paper. Firstly, the fluctuation features extraction method of time series is introduced. By defining and identifying fluctuation points, the fluctuation points sequence is obtained to represent the original time series for subsequent analysis. Then, a new similarity measurement (D_SM) is put forward to calculate the distance between different fluctuation points sequences. This method can calculate the distance of unequal-length time series, and it includes two main steps: similarity matching and the distance calculation based on similarity matching. Finally, the experiments are performed on some public time series using agglomerative hierarchical clustering based on D_SM. Compared to some traditional time series similarity measurements, the clustering results show that the proposed method can effectively distinguish time series with similar shapes from different classes and get a visible improvement in clustering accuracy in terms of F-Measure

Hierarchical Clustering of Time Series Based on Linear Information Granules

Time series clustering is one of the main tasks in time series data mining. In this paper, a new time series clustering algorithm is proposed based on linear information granules. First, we improve the identification method of fluctuation points using threshold set, which represents the main trend information of the original time series. Then using fluctuation points as segmented nodes, we segment the original time series into several information granules, and linear function is used to represent the information granules. With information granulation, a granular time series consisting of several linear information granules replaces the original time series. In order to cluster time series, we then propose a linear information granules based segmented matching distance measurement (LIG_SMD) to calculate the distance between every two granular time series. In addition, hierarchical clustering method is applied based on the new distance (LIG_SMD_HC) to get clustering results. Finally, some public and real datasets about time series are experimented to examine the effectiveness of the proposed algorithm. Specifically, Euclidean distance based hierarchical clustering (ED_HC) and Dynamic Time Warping distance based hierarchical clustering (DTW_HC) are used as the compared algorithms. Our results show that LIG_SMD_HC is better than ED_HC and DTW_HC in terms of F-Measure and Accuracy

Análisis de la efectividad y estabilidad de una combinación de indicadores de Análisis Técnico (Estocástico y el Índice de Fuerza Relativa) en el mercado accionario colombiano en el Período 2009 – 2019

El objetivo de la presente investigación es analizar el efecto de una combinación de indicadores técnicos en el mercado accionario colombiano en términos de efectividad y estabilidad durante el periodo 2009-2019. Para tal fin se utilizaron dos indicadores populares y que han demostrado en diversas investigaciones obtener buenos resultados como el Índice de Fuerza Relativa y el Indicador Estocástico para generar un solo indicador, el cual se va a llamar combinación. Las rentabilidades obtenidas fueron comparadas con la estrategia pasiva y los resultados fueron contrastados con la Hipótesis de Mercados Eficientes y la Teoría de la Caminata aleatoria mediante pruebas de robustez y simulación Bootstrapping para validar la significancia estadística de los resultados. La evidencia empírica de la investigación sugiere que, luego de incluir los costos de transacción, tanto la combinación como los indicadores técnicos por separado no superaron de manera efectiva y estable a la estrategia pasiva.The objective of this research is to analyses the effect of a combination of technical indicators on the Colombian stock market in terms of effectiveness and stability during the 2009-2019 period. For this purpose, two popular indicators were used that have been shown in many researches to obtain good results such as the Relative Strength Index and the Stochastic Indicator to generate a single indicator, this is called combination. The yields obtained were compared with the passive strategy and the results were contrasted with the Efficient-Market Hypothesis and the Theory of the Random Walk through robustness tests and Bootstrapping simulation to validate the statistical significance of the results. Empirical evidence from the research suggests that, after including transaction costs, both the combination and the separate technical indicators do not effectively and stably beat the passive strategy.Línea de Investigación: Gestión FinancieraMaestrí

A novel technical analysis-based method for stock market forecasting

[[abstract]]Owing to the dynamic changes of the stock market and numerous influences on stock prices, assessing stock prices has become increasingly difficult. Furthermore, when dealing with information on stocks, people tend to amplify the importance of available and self-correlative information, a habit that runs contrary to objective and reasonable investment decision-making. Therefore, how to use effective stock information to assist investors in making stock investment decisions is a major topic in stock investment. This study develops a novel technical analysis method for stock market forecasting to effectively promote forecasting accuracy, which can help investors to increase their decision support quality and profitability. Specifically, this study involves the following tasks: (1) design a technical analysis-based stock market forecasting process, (2) develop techniques related to technical analysis-based stock market forecasting, and (3) demonstrate and evaluate the developed technical analysis-based method for stock market forecasting. In developing techniques associated with the technical analysis-based stock market forecasting method, the techniques involve trend-based stock classification, adaptive stock market indicator selection, and stock market trading signal forecasting