# An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis
## 1 Introduction
The rule-based (RB) method is also proposed in the same paper, which uses predefined rules to identify patterns. In [14], [PERSON] et al. designed a real-time pattern matching scheme based on [PERSON]'s rank correlation coefficient and rule sets. These two methods rely on the rules defined for each pattern thus have a disadvantage when we want to update or increase the query pattern, and it takes time to redefine the rules for the new patterns. The Euclidean distance (ED) method can also be used to calculate the similarity of two patterns and it does not need to be segmented, but from the previous experiments we can see that the ED approach has bad performance regard to some distorted sequences data and does not consider the horizontal and vertical shifts, so the dynamic time warping [15] algorithm (DTW) would be more useful in time-series data processing. However, time sequences are usually long, it would be time-consuming using the distance-based methods without segmentation. Training-based methods consider the pattern matching process as a classic pattern recognition problem. Traditional classification models like support vector machine (SVM) and back-propagation neural network (BPNN) can be applied in time series data classification, the segmentation process is not necessary in these algorithms. Therefore, SVM and BPNN can preserve more information from the raw data [2]. However, this kind of method may have another drawback: These models always need an amount of training data to achieve a high testing accuracy, and it has to learn multiple classifiers for different patterns. Consequently, it is inefficient to apply in real-time financial pattern matching. In this context, we consider using the continuous Hopfield network as our matching approach. The Hopfield neural network (HNN) was proposed by [PERSON] [16] in 1982. The energy function was introduced to study the stability of the network, and it turns out that the HNN has a good memory association ability. The original HNN can only deal with the discrete binary pattern recognition by using Hebb's rule [17] and its memory capacity is limited to the network size [18]. However, in recent years lots of works [19; 20; 21; 22; 23; 24; 25; 26] have studied the memory capacity and invented different kinds of continuous HNN to deal with the continuous value pattern. We leverage HNN's advantage in warping pattern recognition and the segmentation method in our work, proposing a training-based pattern matching approach, which only needs to be trained on the predefined template pattern. With the work mentioned above, we treat the financial time series pattern matching problem as a classic pattern recognition problem. In the next section, we will review the related work in financial pattern matching. In Sections 3.1.1 and 3.1.2, we introduced details about how to leverage the segmentation method and template grid into our matching approach. Section 3.2 presents the algorithm of the learning associate Hopfield network. The content includes the training process and matching procedure of our method. Section 4 describes the experimental data we use and the algorithm that generates the synthetic data. Section 4.2 summarizes the results of the experiments.