Financial Forecasting via Deep-Learning and Machine-Learning Tools over Two-Dimensional Objects Transformed from Time Series

Baldo, Alessandro; Cuzzocrea, Alfredo; Fadda, Edoardo; Bringas, Pablo García

doi:10.1007/978-3-030-86271-8_46

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Lee et al [45] used data from many countries for the purpose of training and testing their model in order to make a prediction about the global stock market using CNNs. Baldo, A., Cuzzocrea et al [46] used the RNN model for forecasting financial marketing.…”

Section: Background Of the Studymentioning

confidence: 99%

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Aldhyani

Alzahrani

2022

Electronics

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The creation of trustworthy models of the equities market enables investors to make better-informed choices. A trading model may lessen the risks that are connected with investing and make it possible for traders to choose companies that offer the highest dividends. However, due to the high degree of correlation between stock prices, analysis of the stock market is made more difficult by batch processing approaches. The prediction of the stock market has entered a technologically advanced era with the advent of technological marvels such as global digitization. For this reason, artificial intelligence models have become very important due to the continuous increase in market capitalization. The novelty of the proposed study is the development of the robustness time series model based on deep leaning for forecasting future values of stock marketing. The primary purpose of this study was to develop an intelligent framework with the capability of predicting the direction in which stock market prices will move based on financial time series as inputs. Among the cutting-edge technologies, artificial intelligence has become the backbone of many different models that predict the direction of markets. In particular, deep learning strategies have been effective at forecasting market behavior. In this article, we propose a framework based on long short-term memory (LSTM) and a hybrid of a convolutional neural network (CNN-LSTM) with LSTM to predict the closing prices of Tesla, Inc. and Apple, Inc. These predictions were made using data collected over the past two years. The mean squared error (MSE), root mean squared error (RMSE), normalization root mean squared error (NRMSE), and Pearson’s correlation (R) measures were used in the computation of the findings of the deep learning stock prediction models. Between the two deep learning models, the CNN-LSTM model scored slightly better (Tesla: R-squared = 98.37%; Apple: R-squared = 99.48%). The CNN-LSTM model showed a superior performance compared with the single deep learning LSTM and existing systems in predicting stock market prices.

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Section: Background Of the Studymentioning

confidence: 99%

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Aldhyani

Alzahrani

2022

Electronics

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“…Though efficient, established cache replacement policies, such as Most-Recently-Used (MRU), LRU, and LFU, are not universally suitable due to their workload dependency and rigid design [9]. Machine Learning (ML) and Deep Learning have proven to be versatile algorithms yielding promising results in various domains, from in-depth biomedical data analysis [4] to financial forecasting [5] and even complex tasks such as satellite image classification [19]. Given their wide range of successful applications, ML and Deep Learning have also been employed in the cache replacement domain to design proficient and effective policies.…”

Section: Introductionmentioning

confidence: 99%

STRCacheML: A Machine Learning-Assisted Content Caching Policy for Streaming Services

Mahara,

Fuentes,

Poellabauer

et al. 2024

Artificial Intelligence and Big Data

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Content caching is vital for enhancing web server efficiency and reducing network congestion, particularly in platforms predicting user actions. Despite many studies conducted toimprove cache replacement strategies, there remains space for improvement. This paper introduces STRCacheML, a Machine Learning (ML) assisted Content Caching Policy. STRCacheML leverages available attributes within a platform to make intelligent cache replacement decisions offline. We have tested various Machine Learning and Deep Learning algorithms to adapt the one with the highest accuracy; we have integrated that algorithm into our cache replacement policy. This selected ML algorithm was employed to estimate the likelihood of cache objects being requested again, an essential factor in cache eviction scenarios. The IMDb dataset, constituting numerous videos with corresponding attributes, was utilized to conduct our experiment. The experimental section highlights our model’s efficacy, presenting comparative results compared to the established approaches based on raw cache hits and cache hit rates.

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“…An ensemble of CNN-LSTM and an ARMA model is utilized for financial time series data in [19]. In [20], a Temporal Convolutional Neural Network (TCNN) model is utilized for the analysis of financial time series data, specifically focusing on applications in Forex markets. This approach is contrasted with Recurrent Neural Networks and other deep learning models, as well as some of the top-performing Machine Learning methods, to demonstrate its effectiveness in handling financial data.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Gap in the Day-Ahead and Real-Time Market Prices Leveraging Exogenous Weather Data

Nizharadze,

Farokhi Soofi,

Manshadi

2023

Algorithms

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Predicting the price gap between the day-ahead Market (DAM) and the real-time Market (RTM) plays a vital role in the convergence bidding mechanism of Independent System Operators (ISOs) in wholesale electricity markets. This paper presents a model to predict the values of the price gap between the DAM and RTM using statistical machine learning algorithms and deep neural networks. In this paper, we seek to answer these questions: What will be the impact of predicting the DAM and RTM price gap directly on the prediction performance of learning methods? How can exogenous weather data affect the price gap prediction? In this paper, several exogenous features are collected, and the impacts of these features are examined to capture the best relations between the features and the target variable. An ensemble learning algorithm, namely the Random Forest (RF), is used to select the most important features. A Long Short-Term Memory (LSTM) network is used to capture long-term dependencies in predicting direct gap values between the markets stated. Moreover, the advantages of directly predicting the gap price rather than subtracting the price predictions of the DAM and RTM are shown. The presented results are based on the California Independent System Operator (CAISO)’s electricity market data for two years. The results show that direct gap prediction using exogenous weather features decreases the error of learning methods by 46%. Therefore, the presented method mitigates the prediction error of the price gap between the DAM and RTM. Thus, the convergence bidders can increase their profit, and the ISOs can tune their mechanism accordingly.

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Financial Forecasting via Deep-Learning and Machine-Learning Tools over Two-Dimensional Objects Transformed from Time Series

Cited by 5 publications

References 13 publications

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

STRCacheML: A Machine Learning-Assisted Content Caching Policy for Streaming Services

Predicting the Gap in the Day-Ahead and Real-Time Market Prices Leveraging Exogenous Weather Data

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