# Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes
## 2 Related work
### Time Series Prediction with CNN  
Unlike previous parametric and nonparametric models for building predictive models through machine learning, CNNs leverage their significant achievements in computer vision classification to address the prediction problems. Following this idea, numerical data are transformed into a knowledge graph for the easy extraction of feature matrices, and then these matrices are fed into CNN to mine behavioral patterns.  
In stock price prediction, [PERSON] and [PERSON] [34] proposed a feature fusion LSTM-CNN model that combines the features of different representations of financial time series data. They constructed four stock chart images as inputs to examine their predictive performance and found that candlestick charts perform the best. To utilize the power of deep CNN for an algorithmic trading system, [PERSON] and [PERSON] [35] proposed a novel approach that converts 1D financial time series into a 2D image-like data representation from 15 selected technical indicators and labeled them as three categories of investment, namely, buy, sell, and hold, depending on the hills and valleys of the original time series. To predict stock market price trends, [PERSON] et. al. [36] dealt with the correlation between stocks by building a company knowledge graph and applied the node2 vec algorithm [37], which has been adopted in many studies [38], to obtain a vector representation of company nodes and to facilitate the subsequent feeding into the CNN for investment pattern recognition.  
Time series classification studies have proposed several techniques for converting the original 1D data into the preferred 2D format to allow CNN to take advantage of the deep CNN classifier. For instance, [PERSON] et al. [39] used recurrence plots (RP) to transform the 1D signals of time series into 2D texture images. [PERSON] and [PERSON] [40; 41] used Gramian angular fields (GAF) and Markov transition fields (MTF) to encode time series signals as images. [PERSON] et al. [42] used Gramian angular summation field (GASF), Gramian angular difference field (GADF), and MTF to encode multivariate time series data into 2D colored images for classification through CNN.  
For the time series prediction problem on traffic flow, [PERSON] et al. [43] represented spatio-temporal traffic flow data as images for the time series prediction problem and then examined the performance of the CNN model. [PERSON] et al. [44] investigated an image-based spatio-temporal data representation approach for predicting traffic flow by using convolutional layers and ensemble learning models. [PERSON] and [PERSON] [45] applied a time series decomposition method to separately feed short-term, long-term, and spatial patterns into different components to identify clusters of neighboring time series residuals and then seeded a single cluster of input time series for each convolutional kernel of neural networks. Conversely, for the time series prediction problem on the stock market, [PERSON] et al. [46] adopted two transformation strategies that test the predictive performance of the CNN model for stock prices by converting the original time series into 2D images via GAF and by computing the moving averages with different time frames to obtain 2D time series expressions and compare them with a candlesticks chart.  
In a comprehensive review of the literature on financial time series forecasting [47], several articles have used raw time series, such as \"open\", \"high\", \"low\", and \"closing\"(OHLC) prices and volume, as their feature set to predict stock prices. This simple approach inspires us to ignore numerous sports performance indicators and statistical information and merely focus on matching-score-related information as our feature set for predicting sports outcomes.