Financial Time Series Forecasting Research Articles

An efficient quantum algorithm for independent component analysis

Abstract Independent component analysis (ICA) is a fundamental data processing technique to decompose the captured signals into as independent as possible components. Computing the contrast function, which serves as a measure of independence of signals, is vital and costs major computing resources in ICA. This paper presents a quantum algorithm that focuses on computing a specified contrast function on a quantum computer. Using the quantum acceleration in matrix operations, we efficiently deal with Gram matrices and estimate the contrast function with the complexity of $O(ε1 --2polylog(N/ε1)). This estimation subprogram, combined with the classical optimization framework, builds up our ICA algorithm, which exponentially reduces the complexity dependence on the data scale compared with algorithms using only classical computers. The outperformance is further supported by numerical experiments, while our algorithm is then applied for the separation of a transcriptomic dataset and for financial time series forecasting, to predict the Nikkei 225 opening index to show its potential application prospect.

Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field and CNNs for Stock Return Predictions

This paper presents the Quantum Gramian Angular Field (QGAF) method, which integrates quantum computing with deep learning to enhance forecasting in time series analysis. This method effectively converts stock return time series data into a format compatible with Convolutional Neural Network (CNN) training. Our empirical tests, conducted on stock market data from China, Hong Kong, and the United States, demonstrate that QGAF significantly outperforms the traditional GAF approach in accuracy. These findings underscore the potential of combining quantum computing and deep learning in financial time series forecasting.

Improved Set Algebra-Based Heuristic Technique for Training Multiplicative Functional Link Artificial Neural Networks for Financial Time Series Forecasting

Improved Set Algebra-Based Heuristic Technique for Training Multiplicative Functional Link Artificial Neural Networks for Financial Time Series Forecasting

Recurrent ensemble random vector functional link neural network for financial time series forecasting

Financial time series forecasting is crucial in empowering investors to make well-informed decisions, manage risks effectively, and strategically plan their investment activities. However, the non-stationary and non-linear characteristics inherent in time series data pose significant challenges when accurately predicting future forecasts. This paper proposes a novel Recurrent ensemble deep Random Vector Functional Link (RedRVFL) network for financial time series forecasting. The proposed model leverages randomly initialized and fixed weights for the recurrent hidden layers, ensuring stability during training. Furthermore, incorporating stacked hidden layers enables deep representation learning, facilitating the extraction of complex patterns from the data. The proposed model generates the forecast by combining the outputs of each layer through an ensemble approach. A comparative analysis was conducted against several state-of-the-art models over financial time-series datasets, and the results demonstrated the superior performance of our proposed model in terms of forecasting accuracy and predictive capability.

Применение методов машинного обучения для анализа показателей на финансовых рынках

The results of research in the field of application of various machine learning (ML) methods for the analysis and forecasting of financial time series are presented. The purpose of the study is to assess the suitability of ML algorithms for solving the problem of analyzing and predicting the state of financial markets. The source data was public data on Apple shares for the period from May 2002 to June 2020. As part of the study, machine learning algorithms were developed using stochastic analysis and data was prepared with subsequent preprocessing for better learning algorithms, as shown in the example of matrix formation for the boosting algorithm. The area of forecasting financial time series was studied and algorithms of boosting, fully connected neural network, recurrent neural network, neural networks with long short-term memory, convolutional neural network were applied. Based on the trained algorithms, a comparative analysis was carried out on a sample of financial instruments from one sector

Fin-GAN: forecasting and classifying financial time series via generative adversarial networks

We investigate the use of Generative Adversarial Networks (GANs) for probabilistic forecasting of financial time series. To this end, we introduce a novel economics-driven loss function for the generator. This newly designed loss function renders GANs more suitable for a classification task, and places them into a supervised learning setting, whilst producing full conditional probability distributions of price returns given previous historical values. Our approach moves beyond the point estimates traditionally employed in the forecasting literature, and allows for uncertainty estimates. Numerical experiments on equity data showcase the effectiveness of our proposed methodology, which achieves higher Sharpe Ratios compared to classical supervised learning models, such as LSTMs and ARIMA.

A multiattribute financial time series forecast model based on double hierarchy fuzzy linguistic term set

Based on the double hierarchy linguistic term sets (DHLTS), a novel forecasting model is proposed considering both the internal fluctuation rules and the external correlation of different time series. The innovative aspects of this model consist of: (i) It can expresses more internal fluctuation and external correlation information, providing guarantees for improving the predictive performance of the model. (ii) The equivalent transformation function of DHLTS reduces the fuzzy granularity and improves the prediction accuracy. (iii) The application of similarity measures can extract the closest rules from historical states based on the distance operators of DHLTS. In addition, experiments on TAIEX considering the impact of the U.S. stock market and other data show that the model has good predictive performance.

Expert aggregation for financial forecasting

Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest. But choosing between several algorithms can be challenging, as their estimation accuracy may be unstable over time. Online aggregation of experts combine the forecasts of a finite set of models in a single approach without making any assumption about the models. In this paper, a Bernstein Online Aggregation (BOA) procedure is applied to the construction of long-short strategies built from individual stock return forecasts coming from different machine learning models. The online mixture of experts leads to attractive portfolio performances even in non-stationary environments. The inclusion of neural networks experts in the aggregation contributes to a better average return, while Ordinary Least Squares with Huber Loss experts contribute to lower risk. The aggregation outperforms individual algorithms, offering a higher portfolio Sharpe ratio, lower shortfall, with a similar turnover. Extensions to expert and aggregation specialisations are also proposed to improve the overall mixture on a family of portfolio evaluation metrics.

Adversarial unsupervised domain adaptation based on generative adversarial network for stock trend forecasting

Stock trend forecasting, which refers to the prediction of the rise and fall of the next day’s stock price, is a promising research field in financial time series forecasting, with a large quantity of well-performing algorithms and models being proposed. However, most of the studies focus on trend prediction for stocks with a large number of samples, while the trend prediction problem of newly listed stocks with only a small number of samples is neglected. In this work, we innovatively design a solution to the Small Sample Size (SSS) trend prediction problem of newly listed stocks. Traditional Machine Learning (ML) and Deep Learning (DL) techniques are based on the assumption that the available labeled samples are substantial, which is invalid for SSS trend prediction of newly listed stocks. In order to break out of this dilemma, we propose a novel Adversarial Unsupervised Domain Adaptation Network (AUDA-Net), based on Generative Adversarial Network (GAN), ad hoc for SSS stock trend forecasting. Different from the traditional domain adaptation algorithms, we employ a GAN model, which is trained on basis of the target stock dataset, to effectively solve the absence problem of available samples. Notably, AUDA-Net can reasonably and successfully transfer the knowledge learned from the source stock dataset to the newly listed stocks with only a few samples. The stock trend forecasting performance of our proposed AUDA-Net model has been verified through extensive experiments conducted on several real stock datasets of the U.S. stock market. Using stock trend forecasting as a case study, we show that the SSS forecasting results produced by AUDA-Net are favorably comparable to the state-of-the-art.

Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022

AbstractAccurately predicting the prices of financial time series is essential and challenging for the financial sector. Owing to recent advancements in deep learning techniques, deep learning models are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. This shift in model selection has led to a notable rise in research related to applying deep learning models to price forecasting, resulting in a rapid accumulation of new knowledge. Therefore, we conducted a literature review of relevant studies over the past 3 years with a view to aiding researchers and practitioners in the field. This review delves deeply into deep learning‐based forecasting models, presenting information on model architectures, practical applications, and their respective advantages and disadvantages. In particular, detailed information is provided on advanced models for price forecasting, such as Transformers, generative adversarial networks (GANs), graph neural networks (GNNs), and deep quantum neural networks (DQNNs). The present contribution also includes potential directions for future research, such as examining the effectiveness of deep learning models with complex structures for price forecasting, extending from point prediction to interval prediction using deep learning models, scrutinizing the reliability and validity of decomposition ensembles, and exploring the influence of data volume on model performance.This article is categorized under: Technologies > Prediction Technologies > Artificial Intelligence

Machine learning-based approaches for financial market prediction: A comprehensive review

This research paper investigates the use of machine learning techniques in financial markets. The paper provides a comprehensive literature review of recent research on machine learning applications in finance, including stock price prediction, financial time series forecasting, and portfolio optimization. Various machine learning techniques, such as regression analysis, decision trees, support vector machines, and deep learning, are discussed in detail, with a focus on their strengths, weaknesses, and potential applications. The paper also highlights the challenges associated with machine learning in finance, such as data quality, model interpretability, and ethical considerations. Overall, the paper demonstrates that machine learning has significant potential in finance but calls for further research to address these challenges and fully explore its potential in financial markets.

Can Artificial Neural Networks Be Used to Predict Bitcoin Data?

Financial markets are complex, evolving dynamic systems. Due to their irregularity, financial time series forecasting is regarded as a rather challenging task. In recent years, artificial neural network applications in finance for such tasks as pattern recognition, classification, and time series forecasting have dramatically increased. The objective of this paper is to present this versatile framework and attempt to use it to predict the stock return series of four public-listed companies on the New York Stock Exchange. Our findings coincide with those of Burton Malkiel in his book, A Random Walk Down Wall Street; no conclusive evidence is found that our proposed models can predict the stock return series better than that of a random walk.

Aspects of Developing A Methodology for Managing Digital Financial Assets

The purpose of the study is to highlight the key aspects necessary for the formation of a methodology for designing systems for managing trading operations over cryptocurrencies. The methodology of designing digital asset management systems defines a set of rules for using methods, models and algorithms required to build systems that solve the complex problem of managing trading operations over digital economy assets. For this purpose, the dynamics and trends of pricing of digital investment and financial assets are investigated to identify the peculiarities and specific patterns of cryptocurrency, taking into account the mathematical model of issuance in the conditions of a dynamic crypto-asset market, functioning continuously and forming cyclicality and wave structures.Materials and methods. The object of the study is the dynamics of value indicators of the market of digital financial assets and digital currencies. The subject of the study is methods, models and algorithms for automated monitoring and management of digital financial assets and cryptocurrencies. The methodological basis of the research is formed by blockchain technologies, modeling and mathematical statistics methods, artificial intelligence methods. The statistical information base of the research is formed on the basis of the history of Tradingview’s value quotations from international trading exchanges of digital currencies, as well as by Coinmarketcap and Coingecko cryptoasset market integrators. Problems of modeling processes in economic systems are considered, the problem of optimal control is defined. A critical analysis of the state in the tasks of economic modeling is carried out, taking into account the uncertainty caused by social and psychological reasons.Results. The proposed methodology offers a comprehensive solution to the problem of managing digital financial assets and other financial instruments based on blockchain technology. Digital currencies act as the management object of the proposed methodology, the initial information is a vector of parameters that determine the sensitivity of the system to the perturbing influences of the external environment and the requirements for the expected management results. In accordance with the scientific novelty of the research and methodology, a set of interrelated research stages is formed, consisting of an ordered cascade of methods, models and algorithms that perform preliminary analysis, processing and forecasting of financial time series of value indicators.Conclusion. New blockchain technologies and the emergence of Chat GPT (generative pre-trained transformer) pose new challenges to society, which hopes to utilize such solutions, including for economic tasks. With the help of prediction-free methods and artificial neural networks it is possible to design software systems, thanks to which it is possible to increase the efficiency of trading operations at optimal risks in automatic and automated mode of trade execution. The proposed methodology of management with auxiliary intellectual analysis of time series and application of combined method of decision-making allows managing the portfolio of a new asset class of digital currencies based on blockchain technologies. Taking into account the technical similarity of digital currencies with digital financial assets, it is possible to use the methodology also for the creation of digital financial asset management systems.

Application of VMD–SSA–BiLSTM algorithm to smart grid financial market time series forecasting and sustainable innovation management

Introduction: This paper proposes a deep learning algorithm based on the VMD-SSA-BiLSTM model for time series forecasting in the smart grid financial market. The algorithm aims to extract useful information from power grid signals to improve the timing prediction accuracy and meet the needs of sustainable innovation management.Methods: The proposed algorithm employs the variational mode decomposition (VMD) method to decompose and reduce the dimensionality of historical data, followed by singular spectrum analysis (SSA) to perform singular spectrum analysis on each intrinsic mode function component. The resulting singular value spectrum matrices serve as input to a bidirectional long short-term memory (BiLSTM) neural network, which learns the feature representation and prediction model of the smart grid financial market through forward propagation and backpropagation.Results: The experimental results demonstrate that the proposed algorithm effectively predicts the smart grid financial market's time series, achieving high prediction accuracy and stability. The approach can contribute to sustainable innovation management and the development of the smart grid.Discussion: The VMD-SSA-BiLSTM algorithm's efficiency in extracting useful information from power grid signals and avoiding overfitting can improve the accuracy of timing predictions in the smart grid financial market. The algorithm's broad application prospects can promote sustainable innovation management and contribute to the development of the smart grid.

A Comprehensive Survey on Higher Order Neural Networks and Evolutionary Optimization Learning Algorithms in Financial Time Series Forecasting

A Comprehensive Survey on Higher Order Neural Networks and Evolutionary Optimization Learning Algorithms in Financial Time Series Forecasting

Financial Time Series Forecasting: A Data Stream Mining-Based System

Data stream mining (DSM) represents a promising process to forecast financial time series exchange rate. Financial historical data generate several types of cyclical patterns that evolve, grow, decrease, and end up dying. Within historical data, we can notice long-term, seasonal, and irregular trends. All these changes make traditional static machine learning models not relevant to those study cases. The statistically unstable evolution of financial market behavior yields a progressive deterioration in any trained static model. Those models do not provide the required characteristics to evolve continuously and sustain good forecasting performance as the data distribution changes. Online learning without DSM mechanisms can also miss sudden or quick changes. In this paper, we propose a possible DSM methodology, trying to cope with that instability by implementing an incremental and adaptive strategy. The proposed algorithm includes the online Stochastic Gradient Descent algorithm (SGD), whose weights are optimized using the Particle Swarm Optimization Metaheuristic (PSO) to identify repetitive chart patterns in the FOREX historical data by forecasting the EUR/USD pair’s future values. The data trend change is detected using a statistical technique that studies if the received time series instances are stationary or not. Therefore, the sliding window size is minimized as changes are detected and maximized as the distribution becomes more stable. Results, though preliminary, show that the model prediction is better using flexible sliding windows that adapt according to the detected distribution changes using stationarity compared to learning using a fixed window size that does not incorporate any techniques for detecting and responding to pattern shifts.

Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?

A reliable crude oil price forecast is important for market pricing. Despite the widespread use of ensemble empirical mode decomposition (EEMD) in financial time series forecasting, the one-time decomposition on the entire time series leads the in-sample data to be affected by the out-of-sample data. Consequently, the forecasting accuracy is overstated. This study incorporates a rolling window into two prevalent EEMD-based modeling paradigms, namely decomposition-ensemble and denoising, to ensure that only in-sample time series is processed by EEMD and used for model training. Given the time-consuming process of stepwise preprocessing and model fitting, two non-iterative machine learning algorithms, random vector functional link (RVFL) neural network and extreme learning machine (ELM), are used as predictors. Hence, we develop the rolling decomposition-ensemble and rolling denoising paradigms, respectively. Contrary to the majority of prior studies, empirical results based on monthly spot price time series for the Brent and West Texas Intermediate (WTI) markets indicate that EEMD plays a weak role in improving crude oil price forecasts when only the in-sample set is preprocessed. This is compatible with the weak form of the efficient market hypothesis (EMH). Nevertheless, the suggested rolling EEMD-denoising model has an advantage over other employed models for long-term forecasting.

A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting

Accurate and real-time forecasting of the price of oil plays an important role in the world economy. Research interest in forecasting this type of time series has increased considerably in recent decades, since, due to the characteristics of the time series, it was a complicated task with inaccurate results. Concretely, deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have appeared in this field with promising results compared to traditional approaches. To improve the performance of existing networks in time series forecasting, in this work two types of neural networks are brought together, combining the characteristics of a Graph Convolutional Network (GCN) and a Bidirectional Long Short-Term Memory (BiLSTM) network. This is a novel evolution that improves existing results in the literature and provides new possibilities in the analysis of time series. The results confirm a better performance of the combined BiLSTM-GCN approach compared to the BiLSTM and GCN models separately, as well as to the traditional models, with a lower error in all the error metrics used: the Root Mean Squared Error (RMSE), the Mean Squared Error (MSE), the Mean Absolute Percentage Error (MAPE) and the R-squared (R2). These results represent a smaller difference between the result returned by the model and the real value and, therefore, a greater precision in the predictions of this model.

Data-driven Methods for Simulation and Forecasting of Financial Time Series

Data-driven Methods for Simulation and Forecasting of Financial Time Series

Wavelet analysis and forecasting of financial time series

Models and methods of forecasting financial time series are considered in the work. The main advantages and disadvantages of traditional models and neural networks for forecasting without data preprocessing are analyzed. Wavelet analysis and a recurrent neural network with long short-term memory (LSTM) were applied to predict the exchange rate of cryptocurrency. The obtained results are compared with the results of existing approaches, the efficiency is determined and a solution is proposed. Figs.: 6. Tabl.: 2. Refs.: 13 titles.
 Keywords: financial time series, neural networks, forecasting, data preprocessing, wavelet analysis, recurrent neural network with long short-term memory, cryptocurrency exchange rate.