Performance Evaluation of Advanced RNNs for Accurate Prediction of Adjusted Closing Gold Prices

A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in financial forecasting is first examined by comparing it with the multilayer back-propagation (BP) neural network and the regularized radial basis function (RBF) neural network. The variability in performance of SVM with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of financial time series into SVM. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, SVM outperforms the BP neural network in financial forecasting, and there are comparable generalization performance between SVM and the regularized RBF neural network. Furthermore, the free parameters of SVM have a great effect on the generalization performance. SVM with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard SVM in financial forecasting.

This literature review provides a comprehensive overview of key developments in quantitative trading theory, machine learning-based financial time series forecasting, deep learning-based financial time series forecasting, and modern quantitative investment strategies. It highlights seminal contributions from renowned scholars and researchers in the field. This review first explores the Efficient Market Hypothesis (EMH) proposed by Eugene Fama in 1970 and its empirical validation, which lays the foundation for understanding stock market dynamics. It then focuses on the application of machine learning to financial time series forecasting, including Hulls Delta strategy, Hujll Js multifactor model regression series, and Junhua Chens seminal work in 2009, which emphasizes the role of machine learning in solving the challenges of financial time series data. Finally, an overview of the development of deep learning in financial time series forecasting is presented, including the comprehensive model of Bowie et al., the success of Junhua Chens Deep Belief Network (DBN), and the sequence data processing capabilities of the Multi-stage Attention Network (MAN) model. In terms of modern quantitative investment strategies, it covers research areas such as EBIT/EV-based stock ranking studies, higher-order moment analysis, frequent trading strategies, and investor sentiment indicators. In summary, this literature review showcases the evolution of quantitative trading theory, the emergence of machine learning and deep learning in financial time series forecasting, and the development of modern quantitative investment strategies, offering valuable insights and tools for investors, researchers, and practitioners in financial markets.

Financial time series forecasting using independent component analysis and support vector regression

Neural network approach to forecasting of quasiperiodic financial time series

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Financial time series forecasting plays a crucial role in predicting future market trends, pricing assets, and managing risks in financial markets. This paper compares traditional methods, such as ARIMA, Exponential Smoothing, and GARCH, with AI-driven techniques, including machine learning and deep learning models, for financial time series forecasting. Traditional models are well-established and effective for stationary data, but they struggle with non-linear relationships and large datasets. In contrast, AI-driven techniques, such as Random Forests, Long Short-Term Memory Networks (LSTMs), and reinforcement learning, offer improved accuracy and adaptability by capturing complex patterns in the data. However, these models come with higher computational complexity and challenges related to interpretability. The paper provides a comprehensive comparison of these methods, highlighting their strengths, limitations, and practical applications. It concludes by offering recommendations for when to use traditional methods versus AI-driven approaches, based on the nature of the data and forecasting needs. The integration of AI with traditional models is also discussed as a promising future direction in financial forecasting.

Application of support vector machines in financial time series forecasting

Financial time series forecasting is crucial for making informed investment decisions. This study proposes an improvised method utilizing a Neuro-Fuzzy System (NFS) for enhanced forecasting accuracy. Traditional forecasting methods often struggle with the nonlinear and dynamic nature of financial time series data. NFS integrates neural network and fuzzy logic techniques, offering a robust framework for modeling complex relationships within financial data. The proposed method employs NFS to adaptively learn and model the intricate patterns present in financial time series data. It combines the strengths of neural networks in learning complex patterns and fuzzy logic in handling uncertainty and imprecision. This study contributes by introducing an innovative approach to financial time series forecasting, leveraging the capabilities of NFS to improve forecasting accuracy and reliability. Experimental results demonstrate the effectiveness of the proposed method in accurately forecasting financial time series data. The method outperforms traditional forecasting techniques, showcasing its potential for practical applications in financial markets.

Because the financial time series are inherently noisy,non-stationary and deterministicaUy chaotic, it is difficult to describe this system by traditional methods.The authors presented a novel load forecasting method i.e.an improved Support Vector Machines(SVM).The object of this paper is to examine the feasibility of SVM in financial time series forecasting by comparing it with a back-propagation(BP)neural network.Analysis of the experiment results proves that it is advantageous to apply SVMs to forecast fmancial time series.

The inherent challenges of financial time series forecasting demand advanced modeling techniques for reliable predictions. Effective financial time series forecasting is crucial for financial risk management and the formulation of investment decisions. The accurate prediction of stock prices is a subject of study in the domains of investing and national policy. This problem appears to be challenging due to the presence of multi-noise, nonlinearity, volatility, and the chaotic nature of stocks. This paper proposes a novel financial time series forecasting model based on the deep learning ensemble model LSTM-mTrans-MLP, which integrates the long short-term memory (LSTM) network, a modified Transformer network, and a multilayered perception (MLP). By integrating LSTM, the modified Transformer, and the MLP, the suggested model demonstrates exceptional performance in terms of forecasting capabilities, robustness, and enhanced sensitivity. Extensive experiments are conducted on multiple financial datasets, such as Bitcoin, the Shanghai Composite Index, China Unicom, CSI 300, Google, and the Amazon Stock Market. The experimental results verify the effectiveness and robustness of the proposed LSTM-mTrans-MLP network model compared with the benchmark and SOTA models, providing important inferences for investors and decision-makers.

As the complexity of financial markets continues to increase, traditional financial time series forecasting methods face huge challenges. Machine learning, especially deep learning, has become a powerful tool to meet this challenge. However, these models often require a lot of computing resources when processing large-scale data, resulting in delays in the training and prediction process. In order to improve the efficiency and real-time performance of prediction, machine learning algorithm acceleration technology has emerged, mainly through hardware acceleration and software optimization to improve the speed of model training and reasoning. This study explores the application of machine learning algorithm acceleration technology in financial time series forecasting, analyzes how acceleration technology can help solve problems in data processing, real-time forecasting, and other aspects, and demonstrates the actual effects of these technologies in the stock market, foreign exchange market, and commodity market through case analysis. Despite the challenges of data diversity and hardware resource limitations, future research directions will focus on the combination of deep reinforcement learning, cloud computing, edge computing, and quantum computing to further promote the intelligent and efficient development of financial time series forecasting.

This article proposes a lightweight deep learning model based on residual thinking, RAR-LSTM (Residual and Regime aware LSTM), to address the difficult issues of high noise, non stationarity, and state switching in economic and financial time series forecasting. This method uses interpretable naive models as baselines (such as seasonal naive method for quarterly forecasting and random walk method for financial daily frequency forecasting), and only learns the residual part through small-scale LSTM, thus balancing stability and reproducibility. At the same time, introducing minimalist state features (such as quarterly dummy variables, high and low rolling volatility states) helps the model perceive changes in the macro or market environment; In terms of loss function, Pinball Loss is supported for quantile prediction to meet the demand for asymmetric underestimation and overestimation costs in practical business. This article validates the generality of RAR-LSTM in two typical tasks: the macro quarterly task is to predict the US unemployment rate, and the financial daily frequency task is to predict the volatility of S&P 500 days. The experimental results show that RAR-LSTM significantly outperforms methods such as naive baseline, traditional LSTM, and SARIMA in terms of prediction accuracy (MAE, RMSE, etc.), especially in high volatility states where it performs more robustly. This model has the characteristics of simple structure, low computational cost, and easy reproducibility, and is suitable for economic and financial forecasting tasks in teaching, competition, and practical scenarios. Beyond naive baselines, we benchmark against stronger statistical models (ARIMA and ETS) and show that RAR-LSTM remains robust across baselines. We also report quantile forecasts (τ=0.1/0.5/0.9) using Pinball Loss, demonstrating calibrated coverage and practical benefits for risk-aware decisions.

Accurate financial time series forecasting is essential for risk management, portfolio optimization, and trading strategies. Traditional statistical models and classical machine learning approaches often struggle to capture the complex dependencies and volatility of financial markets. Recent advancements in deep learning, particularly transformer-based architectures, have shown significant promise in modeling sequential financial data. However, integrating dynamic risk assessment into forecasting models remains an open challenge. This study proposes a transformer-based financial time series forecasting framework that incorporates adaptive risk metrics to improve predictive accuracy and risk-aware decision-making. The model leverages self-attention mechanisms to capture long-range dependencies in financial data while integrating dynamic volatility measures, value-at-risk (VaR), and conditional value-at-risk (CVaR) as additional input features. By incorporating these adaptive risk factors, the model enhances its ability to anticipate market fluctuations and adjust forecasts accordingly. Experiments on real-world financial datasets demonstrate that the proposed approach outperforms traditional autoregressive models, recurrent neural networks (RNNs), and baseline transformer architectures in terms of predictive accuracy and risk-adjusted performance. The results highlight the importance of integrating risk-sensitive metrics into deep learning-based financial forecasting models, offering a more comprehensive approach for market analysis and investment decision-making.

The work presented here closes a number of important gaps within the body of existing scholarship on financial time series forecasting by giving a thorough and equitable comparison between LSTM and Transformer architectures when tasked with forecasting noisy, scarce datasets which closely model actual conditions. The idea and experimental validation of a Hybrid LSTM–Transformer Network will demonstrate the effectiveness of true innovations, as opposed to optimizing “new objectives,” which are now made possible through such combinations and are effectively coupling memory and attention. Further, the emphasis placed on attention in order to have clearer analysis and better evaluation tools will carry the sub-field of deep learning forwards, more so as regards finance, where it comes nearer to transparency, which, despite its significance, remains a topic that has been neglected in many bibliographic examples, and which highlights the potential importance of model evaluation, in which indicators of success ultimately have more to do with something beyond projection, but the strength, sensibility, and effectiveness of these as well. As algorithmic decision-making becomes increasingly prevalent and enters the system of global trade, allocation, and protection, finance requires assurance of predictable access to forecasting models satisfying these requirements, and therefore this work, and these new paradigms within which it is situated, are central to new applications of finance and new applications of AI.

A novel text mining approach to financial time series forecasting