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Make A Clever Bitcoin Price Chart With React And D3.
Since bitcoin has gained recognition as a valuable asset, researchers have begun to use machine learning to predict bitcoin price. However, because of the impractical cost of hyperparameter optimization, it is greatly challenging to make accurate predictions. In this paper, we analyze the prediction performance trends under various hyperparameter configurations to help them identify the optimal hyperparameter combination with little effort. We employ two datasets which have different time periods with the same bitcoin price to analyze the prediction performance based on the similarity between the data used for learning and future data. With them, we measure the loss rates between predicted values and real price by adjusting the values of three representative hyperparameters. Through the analysis, we show that distinct hyperparameter configurations are needed for a high prediction accuracy according to the similarity between the data used for learning and the future data. Based on the result, we propose a direction for the hyperparameter optimization of the bitcoin price prediction showing a high accuracy.
In the fourth industrial revolution era, cryptocurrencies based on blockchain technology have started to gain popularity as virtual assets. A cryptocurrency uses a peer-to-peer network system where each individual trades assets without a central authority, so it guarantees a secure exchange of currencies and does not require financial fees [1]. Bitcoin, the first issued cryptocurrency, has the largest market capitalization [2] among various cryptocurrencies and is regarded as one of their leading representatives. Since most cryptocurrencies are greatly affected by the price changes of bitcoin, it is important to predict the price of bitcoin for assessing the utility and future value of the cryptocurrency market.
In recent years, several neural network models using deep learning have been widely utilized for data prediction by improving technical limitations. These models have also been applied to bitcoin price prediction research. In order to analyze bitcoin data, a long short-term memory (LSTM) model has primarily been employed because bitcoin price is a time series of data. Some researchers have evaluated investment strategies based on bitcoin prediction by using the LSTM model [3]. To make the LSTM model smart, a study took into account external news data obtained from the cryptocurrency market in addition to bitcoin price data [4]. Moreover, several studies analyzed the prediction accuracy and performance of an LSTM model and a recurrent neural network (RNN) model by using bitcoin price data as experimental data [5].
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Deep-learning-based data prediction performance is highly dependent on user-determined model setting values, called hyperparameters. For a high prediction performance, it is crucial to configure the hyperparameter values properly as they have a significant impact on the learning in a prediction model. We need to conduct experiments with all conceivable parameter combinations in order to determine the best combination value for the hyperparameters. However, in terms of time and cost, this brute-force approach is practically impractical. Due to this limitation, existing studies have given up trying to determine the ideal combination of hyperparameters for a high prediction performance. They select a few of the hyperparameters and conduct experiments with the parameters. Furthermore, some of them do not execute experiments with all of the combination of selected parameters to save cost. They determine the optimal value of one of the chosen parameters and use that value to determine the optimal value of the following parameter, even though that value does not produce the best performance when combined with the following parameter.
Additionally, even if the same bitcoin data are used for prediction, different predictive performance values can be found with the same hyperparameter configuration according to the similarity between the data used for learning, training and validation and future data tests. Therefore, hyperparameter optimization should be carried out by considering the similarity in order to achieve high prediction accuracy.
To help users easily find hyperparameter values for the highest prediction performance, we analyze prediction performance trends by adjusting the tunable hyperparameters. We choose three major hyperparameters, time steps, the number of LSTM units and the ratio of dropout layers, for the analysis and use bitcoin price data for the LSTM model. In addition, with the same bitcoin price, we prepare two datasets with distinct data period ranges for training and validation.
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Bitcoin price is a time series collected in a temporal order. A recurrent neural network (RNN), which includes internal recurrent structures, is typically used for these time series data predictions [6]. RNN generates the model’s current state using result values derived from the input values, and at the next point, it obtains a new result value and state by inputting the previous states along with the input values. The neural network updates its weights by adjusting the learning rate applied to the gradient in the backpropagation process. The gradient vanishing or exploding problem arises as the backpropagation process iterates, because the RNN uses the tanh function and matrix multiplication operations [7]. To solve this problem, the LSTM neural network improves the RNN by adding gates that control the state received from the previous time step [8, 9].
Hyperparameters are typically set by users in modeling. Since data predictive performance depends on the values of hyperparameters, the users are required to set them up appropriately.
The loss function, one of the hyperparameters, is used to calculate the errors between the output values of the model and the actual values. By modifying the weights during training, the optimizer supports the loss function by reducing errors. The learning rate is utilized to adjust the degree of the gradient used when the optimizer finds the weights. The number of layers and the number of units of those layers are used to construct a model through a training process. Moreover, the ratio of the dropout layer is a parameter that excludes units at a specific rate to prevent overfitting to the training data [10]. To make effective predictions on real data, an activation function that defines the output of a node given a set of inputs, the epoch that determines the number of iterations of the model learning process and the batch size which decides how much training data to learn [11] are used. The number of time steps is the length of the sequence of data input into the model. Since the RNN proceeds with the prediction from the previous state and produces output values, the user sets the number of time steps which is the length of the input data into the model.
What Is Luna: The Cryptocurrency That Lost 99% Of Its Value?
There are three representative hyperparameters in the LSTM model, number of time steps, the number of internal units in the LSTM layer and the ratio of the dropout layer. For RNNs that deal with time series data, such as LSTM models, how many historical data are used for training greatly affects the prediction performance, so the optimization of the number of time steps is important. In addition, the learning quality of neural networks varies depending on the number of units in the hidden layer inside the neural network. Therefore, the number of units in the LSTM layer influences the quality of the LSTM model. Finally, the ratio of the dropout layer determines how many input values will be removed. We used the hyperparameters to make the model not excessively focus on training data. The predicted values and actual ones differed significantly if the three hyperparameters were not set to the proper values.
Figure 1 shows the predicted values obtained by arbitrarily specifying two hyperparameters, the number of LSTM units and the dropout ratio with seven time steps, in an LSTM model. We set the number of LSTM units and ratio of the dropout layer to (256, 0.05), (8, 0.3) and (2, 0.5), respectively, and compared three predicted values to the actual bitcoin price data. As can be seen in Figure 1, (256, 0.5) shows similar predicted values to the actual data, but (8, 0.3) and (2, 0.5) show predicted values significantly different from the actual data. With these experiments, we proved that the LSTM model’s predictive accuracy depends on the hyperparameter settings,
Bitcoin price is a time series collected in a temporal order. A recurrent neural network (RNN), which includes internal recurrent structures, is typically used for these time series data predictions [6]. RNN generates the model’s current state using result values derived from the input values, and at the next point, it obtains a new result value and state by inputting the previous states along with the input values. The neural network updates its weights by adjusting the learning rate applied to the gradient in the backpropagation process. The gradient vanishing or exploding problem arises as the backpropagation process iterates, because the RNN uses the tanh function and matrix multiplication operations [7]. To solve this problem, the LSTM neural network improves the RNN by adding gates that control the state received from the previous time step [8, 9].
Hyperparameters are typically set by users in modeling. Since data predictive performance depends on the values of hyperparameters, the users are required to set them up appropriately.
The loss function, one of the hyperparameters, is used to calculate the errors between the output values of the model and the actual values. By modifying the weights during training, the optimizer supports the loss function by reducing errors. The learning rate is utilized to adjust the degree of the gradient used when the optimizer finds the weights. The number of layers and the number of units of those layers are used to construct a model through a training process. Moreover, the ratio of the dropout layer is a parameter that excludes units at a specific rate to prevent overfitting to the training data [10]. To make effective predictions on real data, an activation function that defines the output of a node given a set of inputs, the epoch that determines the number of iterations of the model learning process and the batch size which decides how much training data to learn [11] are used. The number of time steps is the length of the sequence of data input into the model. Since the RNN proceeds with the prediction from the previous state and produces output values, the user sets the number of time steps which is the length of the input data into the model.
What Is Luna: The Cryptocurrency That Lost 99% Of Its Value?
There are three representative hyperparameters in the LSTM model, number of time steps, the number of internal units in the LSTM layer and the ratio of the dropout layer. For RNNs that deal with time series data, such as LSTM models, how many historical data are used for training greatly affects the prediction performance, so the optimization of the number of time steps is important. In addition, the learning quality of neural networks varies depending on the number of units in the hidden layer inside the neural network. Therefore, the number of units in the LSTM layer influences the quality of the LSTM model. Finally, the ratio of the dropout layer determines how many input values will be removed. We used the hyperparameters to make the model not excessively focus on training data. The predicted values and actual ones differed significantly if the three hyperparameters were not set to the proper values.
Figure 1 shows the predicted values obtained by arbitrarily specifying two hyperparameters, the number of LSTM units and the dropout ratio with seven time steps, in an LSTM model. We set the number of LSTM units and ratio of the dropout layer to (256, 0.05), (8, 0.3) and (2, 0.5), respectively, and compared three predicted values to the actual bitcoin price data. As can be seen in Figure 1, (256, 0.5) shows similar predicted values to the actual data, but (8, 0.3) and (2, 0.5) show predicted values significantly different from the actual data. With these experiments, we proved that the LSTM model’s predictive accuracy depends on the hyperparameter settings,
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