Development of recurrent neural networks for price forecasting at cryptocurrency exchanges

Stock price forecasting using PSO hypertuned neural nets and ensembling

Bitcoin's decentralized nature has made it a popular mode of payment for buyers and sellers, but its highly volatile nature poses a challenge for investors. This study aims to predict future Bitcoin prices using a combination of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. The non-stationary nature of Bitcoin prices is addressed using RNN, which is particularly useful for analyzing sequential data.The dataset used in this research is sourced from the Kraken exchange and includes various factors that are believed to influence Bitcoin prices, such as transaction volume, hash rate, and Google search trends. The data is preprocessed and cleaned to ensure accuracy, and then fed into the RNN and LSTM models for training and testing.The study's use of RNN and LSTM algorithms demonstrates the effectiveness of these methods in predicting Bitcoin prices, particularly in the context of sequential data. The results of the study, provide insights into potential future trends in Bitcoin prices and identify key indicators that significantly influence Bitcoin prices. The findings of this research have important implications for investors and traders looking to make informed decisions in the cryptocurrency market, as well as for researchers seeking to improve our understanding of Bitcoin's price dynamics. By predicting future prices, the study provides insights that can mitigate the risks associated with Bitcoin's volatility, making it a more viable investment option.

In order to solve the problems of unbalanced sample data and the lack of consideration of temporal information in existing Siamese-based trackers, this paper proposes a Siamese recurrent neural network and region proposal network (Siamese R-RPN), which can be trained in an end-to-end manner. Siamese R-RPN is consisted of Siamese network, recurrent neural network and region proposal network. Image features extracted by the Siamese network are strengthened by the channel and spatial attention mechanisms, and are sent to the RPN for classification and regression. Temporal information is processed by a recurrent neural network-based Long Short-Term Memory (LSTM) to predict the rough location of the target, it is mapped to the anchor feature map of the RPN for anchor selection. This makes the positive and negative samples participating in the training procedure to become more balanced and representative. Because of the collaborative use of temporal and spatial information, the tracker proposed in this paper has achieved state-of-the-art performance on three large tracking benchmarks—OTB 2015, VOT2016 and VOT 2018—where this verifies its effectiveness.

In this paper, we propose the use of recurrent neural networks (RNNs) to develop an effective solution to two problems in electrocardiogram (ECG)-based biometrics: identification/classification and authentication. Different RNN architectures with various parameter settings were evaluated, including traditional, long short-term memory (LSTM), gated recurrent unit (GRU), unidirectional, and bidirectional networks. Unlike many existing methods, the RNN-based method does not require any feature extraction. The method was evaluated using two publicly available datasets: ECG-ID and MIT-BIH Arrhythmia (MITDB). For the identification problem, nearly 100% classification accuracy on the ECG-ID dataset was achieved, and similar results were observed for the MITDB dataset. For the authentication problem, an RNN was trained and the hidden state at the final time step was extracted to make a decision. We evaluated the effect of the training size on the equal error rate (EER), and showed that the EER drops from approximately 3.5% to nearly 0% as we increased the percentage of subjects used for training from approximately 15% to 80%.

Prediction concept needs human-like thinking and ideas. ANN helps make a program for describing the solutions to the prediction problems. To gain accurate prediction, there is a need for deep thinking in the knowledge system discovered. Deep learning helps form such next-level type of ANN thinking. Deep learning models do not require humans for programming the problems. It can determine the prediction by itself by learning the dataset. This paper describes the prediction by considering sequential model for continuous accessing of data with the help of multi-layers. Deep learning is very helpful for predicting larger dataset. This paper proposes an algorithmic model known as a Comprehensive Deep Recurrent Artificial Neural Networks (CDRANN) by hybridizing LSTM (long short-term memory) with RNN (recurrent neural network) backtrack solver for better future stock prediction. This paper also explains the need for hybridizing evolutionary model CDRANN to create a novel change in future prediction.KeywordsCDRANN (Comprehensive Deep Recurrent Artificial Neural Networks)LSTM (long short-term memory)RNN (recurrent neural network)Stock marketPrediction

Recurrent Neural Networks (RNNs), which are a powerful scheme for modeling temporal and sequential data need to capture long-term dependencies on datasets and represent them in hidden layers with a powerful model to capture more information from inputs. For modeling long-term dependencies in a dataset, the gating mechanism concept can help RNNs remember and forget previous information. Representing the hidden layers of an RNN with more expressive operations (i.e., tensor products) helps it learn a more complex relationship between the current input and the previous hidden layer information. These ideas can generally improve RNN performances. In this paper, we proposed a novel RNN architecture that combine the concepts of gating mechanism and the tensor product into a single model. By combining these two concepts into a single RNN, our proposed models learn long-term dependencies by modeling with gating units and obtain more expressive and direct interaction between input and hidden layers using a tensor product on 3-dimensional array (tensor) weight parameters. We use Long Short Term Memory (LSTM) RNN and Gated Recurrent Unit (GRU) RNN and combine them with a tensor product inside their formulations. Our proposed RNNs, which are called a Long-Short Term Memory Recurrent Neural Tensor Network (LSTMRNTN) and Gated Recurrent Unit Recurrent Neural Tensor Network (GRURNTN), are made by combining the LSTM and GRU RNN models with the tensor product. We conducted experiments with our proposed models on word-level and character-level language modeling tasks and revealed that our proposed models significantly improved their performance compared to our baseline models.

Recurring wildfires pose a critical global issue as they undermine social and economic stability and jeopardize human lives. To effectively manage disasters and bolster community resilience, the development of wildfire susceptibility maps (WFSMs) has emerged as a crucial undertaking in recent years. In this research endeavor, two deep learning algorithms were leveraged to generate WFSMs using two distinct remote sensing datasets. Specifically, the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat-8 images were utilized to monitor wildfires that transpired during the year 2021. To develop an effective WFSM, two datasets were created by incorporating 599 wildfire locations with Landsat-8 images and 232 sites with MODIS images, as well as twelve factors influencing wildfires. Deep learning algorithms, namely the long short-term memory (LSTM) and recurrent neural network (RNN), were utilized to model wildfire susceptibility using the two datasets. Subsequently, four WFSMs were generated using the LSTM (MODIS), LSTM (Landsat-8), RNN (MODIS), and RNN (Landsat-8) algorithms. The evaluation of the WFSMs was performed using the area under the receiver operating characteristic (ROC) curve (AUC) index. The results revealed that the RNN (MODIS) (AUC = 0.971), RNN (Landsat-8) (AUC = 0.966), LSTM (MODIS) (AUC = 0.964), and LSTM (Landsat-8) (AUC = 0.941) algorithms demonstrated the highest modeling accuracy, respectively. Moreover, the Gini index was employed to assess the impact of the twelve factors on wildfires in the study area. The results of the random forest (RF) algorithm indicated that temperature, wind speed, slope, and topographic wetness index (TWI) parameters had a significant effect on wildfires in the study region. These findings are instrumental in facilitating efficient wildfire management and enhancing community resilience against the detrimental effects of wildfires.

We explore the architecture of recurrent neural networks (RNNs) by studying the complexity of string sequences it is able to memorize. Symbolic sequences of different complexity are generated to simulate RNN training and study parameter configurations with a view to the network's capability of learning and inference. We compare Long Short-Term Memory (LSTM) networks and gated recurrent units (GRUs). We find that an increase in RNN depth does not necessarily result in better memorization capability when the training time is constrained. Our results also indicate that the learning rate and the number of units per layer are among the most important hyper-parameters to be tuned. Generally, GRUs outperform LSTM networks on low-complexity sequences while on high-complexity sequences LSTMs perform better.

Photovoltaic power generation is one of the most efficient renewable energy generation techniques. To ensure photovoltaic (PV) safe operation and cost-effective integration into smart grids. As a result, a realistic PV power forecast method can aid in mitigating PV power generation’s drawbacks, which is critical for power plant maintenance and repair. In this research, we compared two Deep Learning models which are both highly advantageous, namely a long short-term memory (LSTM) and a recurrent neural network (RNN). A case study employing an actual dataset obtained from 1B DKASC, Alice Springs, Australia, is used to demonstrate the performance of LSTM and RNN. The forecasts generated by the LSTM are compared with the results of RNN using MAE, MSE, MBE, R\(^2\) and RMSE for all the forecast horizons. The results of the study demonstrate that the LSTM based forecast outperforms RNN and has the potential to improve the accuracy and stability of the prediction.KeywordsDeep LearningLong Short-Term Memory (LSTM)Recurrent neural network (RNN)

One of the most promising tools in recent years for the analysis and prediction of time series data, which includes financial market data has been the use of neural networks. To ensure the accuracy of the outcomes of these systems, it is critical to overcome the vanishing gradient and exploding gradient problems that often occur when recurrent neural networks (RNN) process data. Long Short-Term Memory (LSTM) has been shown to provide good performance when dealing with time series datasets. This paper will explore the feasibility of using an RNN with LSTM as a predictive tool for use with futures trading data. Using a dataset comprised of all futures trading occurring on the Bourse de MontrA©al (TMX) during a 9-month period from January to September 2015, we assessed the predictive effectiveness of an RNN in predicting the price of front-end contracts for the futures symbol BAX. We found that while an RNN provided a degree of short-term predictive capability, this capability did not extend beyond a couple of days. Although it failed as a trading instrument to predict futures prices, the RNN could detect, identify, and reflect underlying trends in the data, indicating the tool may hold promise in the detection of trading patterns.Â

Recurrent neural networks (RNN) are at the core of modern automatic speech recognition (ASR) systems. In particular, long short-term memory (LSTM) recurrent neural networks have achieved state-of-the-art results in many speech recognition tasks, due to their efficient representation of long and short term dependencies in sequences of inter-dependent features. Nonetheless, internal dependencies within the element composing multidimensional features are weakly considered by traditional real-valued representations. We propose a novel quaternion long short-term memory (QL-STM) recurrent neural network that takes into account both the external relations between the features composing a sequence, and these internal latent structural dependencies with the quaternion algebra. QLSTMs are compared to LSTMs during a memory copy-task and a realistic application of speech recognition on the Wall Street Journal (WSJ) dataset. QLSTM reaches better performances during the two experiments with up to 2.8 times less learning parameters, leading to a more expressive representation of the information.

In dynamic bandwidth management based on traffic prediction, the traffic flows can be modeled as time-series data. State-of-the-art technique used in modeling this traffic flows is by using a linear model. In contrast, Recurrent Neural Network (RNN) has been the state-of-the-art technique in speech recognition, which data is also time-series. Therefore, we conjecture that the use of RNN can improve performance in dynamic bandwidth management based on traffic prediction. In this paper, we employ a variant of RNN called Deep Long Short Term Memory (DLSTM), which is common to be used in speech recognition. The result of this work shows that DLSTM is suitable for traffic prediction and is able to decrease packet loss ratio of a network system simulated using Network Simulator 3 (NS3).

AC contactors are used frequently in various low-voltage control lines, so remaining-life prediction for them can significantly improve the operational reliability of power control systems. To address the problem that the existing AC contactor remaining-life prediction methods do not make full use of the correlation between previous and later states in the degradation process, a CNN-GRU (convolutional neural network-gated recurrent unit) method for AC-contactor remaining-life prediction is proposed. Firstly, the entire cycle of an AC contactor’s degradation data is obtained through a whole-life test, from which the characteristic parameters that effectively reflect the operating states of the contactor are extracted; secondly, neighborhood component analysis (NCA) and maximal information coefficient (MIC) are used to eliminate the redundant information of multidimensional parameters in order to select the optimal feature subset; and then, CNN is used to compress the feature dimension and mine the regular information between the features, so as to extract the effective feature vectors; finally, taking the AC contactor remaining electrical life as a long time sequence issue, time-series accurate prediction is performed using GRU. It is verified that this model is better than RNN (recurrent neural network), LSTM (long short-term memory) and GRU models in prediction, with an effective accuracy of 96.63%, which effectively supports the feasibility of time-series prediction in the field of the remaining-life prediction of electrical devices.

Abstract— Stocks are investments that have dynamic movements. Stock price changes move every day even hourly. With very fast changes, stock prices require predictions to be able to determine stock market projections. Predictions are used to reduce risk when making transactions. In this study, predictions of stock price trends were made using the Recurrent Neural Network (RNN). The approach taken is to perform a time series analysis using the RNN variance, namely Long Short Term Memory (LSTM). Hyperparameter construction in the LSTM model testing simulation can estimate stock prices with maximum percentage accuracy. The results showed that the prediction model produced a loss function of 0.0012 and a training time of 73 m/step. The evaluation was carried out with the RMSE which resulted in a score of 17.13325. Predictions are obtained after doing machine learning using 1239 data. The RMSE and LSTM models are calculated by changing the number of epochs, the variation between the predicted stock price and the current stock price. Computations are carried out using a stock market dataset that includes open, high, low, close, adj prices, closes, and volumes. The main objective of this study is to determine the extent to which the LSTM algorithm anticipates stock market prices with better accuracy. Code can be seen at iranihoeronis/RNN-LSTM (github.com) Keywords— Stock Prediction, Time Series, Recurrent Neural Network (RNN), Long Short Term Memory (LSTM).

Stock price returns forecasting is challenging task for day traders to yield more returns. In the past, most of the literature was focused on machine learning algorithm to predict the stock returns. In this work, the recurrent neural network (RNN) with long short term memory (LSTM) is studied to forecast future stock returns. It has the ability to keep the memory of historical stock returns in order to forecast future stock return output. RNN with LSTM is used to store recent stock information than old related stock information. We have considered a recurrent dropout in RNN layers to avoid overfitting in the model. To accomplish the task we have calculated stock return based on stock closing prices. These stock returns are given as input to the recurrent neural network. The objective function of the prediction model is to minimize the error in the model. To conduct the experiment, data is collected from the National Stock Exchange, India (NSE). The proposed RNN with LSTM model outperforms compared to an feed forward artificial neural network.