scholarly journals Forecast of complex financial big data using model tree optimized by bilevel evolution strategy

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Junsuke Senoguchi

AbstractIf a decision tree is constructed through a series of locally optimal solutions, such as the Greedy method, overfitting to the data is likely to occur. In order to avoid overfitting, many previous research have attempted to collectively optimize the structure of a decision tree by using evolutionary computation. However, if attributes of each split and their thresholds are searched simultaneously, the evaluation function becomes intermittent; thus, optimization methods assuming continuous distribution cannot be used. In this study, in order to enable efficient search assuming continuous distribution even for complicated data that contains a lot of noise and extraordinary values, such as financial time series data, the inner level search that optimizes each threshold value collectively given a specific attribute for each split in a model tree and the outer level search that optimizes the attributes of each split were performed by separate evolutionary computing. As a result, we obtained high prediction accuracy that far exceeded the performance of the conventional method.

2021 ◽  
Author(s):  
Junsuke Senoguchi

Abstract If a decision tree is constructed through a series of locally optimal solutions, such as the Greedy method, overfitting to the data is likely to occur. In order to avoid overfitting, many previous research have attempted to collectively optimize the structure of a decision tree by using evolutionary computation. However, if attributes of each split and their thresholds are searched simultaneously, the evaluation function becomes intermittent; thus, optimization methods assuming continuous distribution cannot be used. In this study, in order to enable efficient search assuming continuous distribution even for complicated data that contains a lot of noise and extraordinary values, such as financial time series data, the inner level search that optimizes each threshold value collectively given a specific attribute for each split in a model tree and the outer level search that optimizes the attributes of each split were performed by separate evolutionary computing. As a result, we obtained high prediction accuracy that far exceeded the performance of the conventional method.


2009 ◽  
Vol 36 (2) ◽  
pp. 3761-3773 ◽  
Author(s):  
Robert K. Lai ◽  
Chin-Yuan Fan ◽  
Wei-Hsiu Huang ◽  
Pei-Chann Chang

2021 ◽  
Vol 11 (9) ◽  
pp. 3876
Author(s):  
Weiming Mai ◽  
Raymond S. T. Lee

Chart patterns are significant for financial market behavior analysis. Lots of approaches have been proposed to detect specific patterns in financial time series data, most of them can be categorized as distance-based or training-based. In this paper, we applied a trainable continuous Hopfield Neural Network for financial time series pattern matching. The Perceptually Important Points (PIP) segmentation method is used as the data preprocessing procedure to reduce the fluctuation. We conducted a synthetic data experiment on both high-level noisy data and low-level noisy data. The result shows that our proposed method outperforms the Template Based (TB) and Euclidean Distance (ED) and has an advantage over Dynamic Time Warping (DTW) in terms of the processing time. That indicates the Hopfield network has a potential advantage over other distance-based matching methods.


Author(s):  
Achintya Mukhopadhyay ◽  
Subhashis Datta ◽  
Dipankar Sanyal

The effect of tailpipe friction on the combustion dynamics inside a thermal pulse combustor has been investigated using a nonlinear model consisting of four coupled first order ordinary differential equations. The dynamics of the system is represented through time series plots, time-delay phase plots, and Poincaré maps. The results indicate that as the tailpipe friction factor is lowered, the system undergoes a transition from steady combustion through oscillating combustion to an intermittent combustion with chaotic characteristics before extinction. The time series data are shown to be useful indicator for early detection of extinction. In one approach (thresholding), the occurrence of local peak pressures below a predefined threshold value is identified as an event and the number of events (event count) and largest number of successive cycles with such events (event duration) are recorded as the friction factor is lowered. In another approach, the statistical moments (kurtosis) of the data are used. Number of kurtosis peaks above a prescribed value and variance of the kurtosis values are recorded for decreasing values of friction factor. All these numbers sharply increase as the system approaches extinction.


2016 ◽  
Vol 13 (2) ◽  
pp. 65-75 ◽  
Author(s):  
Alex Bara ◽  
Calvin Mudzingiri

The role of financial innovation on economic growth in developing countries has not been actively pursued. Stemming from the finance-growth nexus, literature suggests that financial innovation has a relationship to growth, which could be either positive or negative. Implicitly, financial innovation has a good and a dark side that affects growth. This study establishes the causal relationship between financial innovation and economic growth in Zimbabwe empirically. Using the Autoregressive Distributed Lag (ARDL) bounds tests and Granger causality tests on financial time series data of Zimbabwe for the period 1980-2013, the study finds that financial innovation has a relationship to economic growth that varies depending on the variable used to measure financial innovation. A long-run, growth-driven financial innovationis confirmed, with causality running from economic growth to financial innovation. Bi-directional causality also exists after conditionally netting-off financial development. Policies that enhance economic growth inter-twined with financial innovation are essential, if developing countries, such as Zimbabwe, aim to maximize economic development


Author(s):  
Malcolm J. Beynonm

The seminal work of Zadeh (1965), namely fuzzy set theory (FST), has developed into a methodology fundamental to analysis that incorporates vagueness and ambiguity. With respect to the area of data mining, it endeavours to find potentially meaningful patterns from data (Hu & Tzeng, 2003). This includes the construction of if-then decision rule systems, which attempt a level of inherent interpretability to the antecedents and consequents identified for object classification (See Breiman, 2001). Within a fuzzy environment this is extended to allow a linguistic facet to the possible interpretation, examples including mining time series data (Chiang, Chow, & Wang, 2000) and multi-objective optimisation (Ishibuchi & Yamamoto, 2004). One approach to if-then rule construction has been through the use of decision trees (Quinlan, 1986), where the path down a branch of a decision tree (through a series of nodes), is associated with a single if-then rule. A key characteristic of the traditional decision tree analysis is that the antecedents described in the nodes are crisp, where this restriction is mitigated when operating in a fuzzy environment (Crockett, Bandar, Mclean, & O’Shea, 2006). This chapter investigates the use of fuzzy decision trees as an effective tool for data mining. Pertinent to data mining and decision making, Mitra, Konwar and Pal (2002) succinctly describe a most important feature of decision trees, crisp and fuzzy, which is their capability to break down a complex decision-making process into a collection of simpler decisions and thereby, providing an easily interpretable solution.


Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 279 ◽  
Author(s):  
Tongle Zhou ◽  
Mou Chen ◽  
Yuhui Wang ◽  
Jianliang He ◽  
Chenguang Yang

To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance, cover and electronic interference and it is related to the state of a target in air combat. Predicting the target intention is helpful to know the target actions in advance. Thus, intention prediction has contributed to lay a solid foundation for air combat decision-making. In this work, an intention prediction method is developed, which combines the advantages of the long short-term memory (LSTM) networks and decision tree. The future state information of a target is predicted based on LSTM networks from real-time series data, and the decision tree technology is utilized to extract rules from uncertain and incomplete priori knowledge. Then, the target intention is obtained from the predicted data by applying the built decision tree. With a simulation example, the results show that the proposed method is effective and feasible for state prediction and intention recognition of aerial targets under uncertain and incomplete information. Furthermore, the proposed method can make contributions in providing direction and aids for subsequent attack decision-making.


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