Stock Forecasting Using Local Data

Alfonso, Gerardo; Carnerero, A. Daniel; Ramı́rez, D.R.; Álamo, T.

doi:10.1109/access.2020.3047160

Cited by 15 publications

(3 citation statements)

References 42 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Emerging nature inspired optimization methods, their unification, implementation, and performance evaluation on single objective benchmark functions are worked out by authors in [55]. A k-step ahead stock forecasting using local data using two techniques such as direct weight optimization and probabilistic approach for computing price intervals are suggested by authors in [56].…”

Section: Related Studiesmentioning

confidence: 99%

Intelligent Financial Forecasting With an Improved Chemical Reaction Optimization Algorithm Based Dendritic Neuron Model

2022

View full text Add to dashboard Cite

Chemical reaction optimization (CRO) algorithm is a robust metaheuristic by simulating the process of natural chemical reaction and has been applied for solving numerous realistic problems. However, it is suffering from low population diversity, slow convergence, and deprived from local search ability. To boost the search operation of CRO, this article proposes an improved CRO method termed as ICRO. We achieve the improvements in two phases. First, to start the search operation with a better-quality population, we propose a new method to initialize population which helps in enhancing diversification of the search space. Second, to intensify the local search ability we integrate Nelder-Mead simplex method with CRO. The ICRO is then evaluated on a few benchmark functions for optimization and found apposite. Dendritic neuron model (DNM) using additive and multiplicative-based aggregation functions has been emerging as a machine learning approach and found successful in many engineering applications. This study attempts to advance the predictive accuracy of DNM through maintaining a decent steadiness between exploitation and exploration of its search space with the proposed ICRO, termed as ICRO-DNM. The powerful global search ability of ICRO synergies with better approximation capability of DNM thus, able to overcome the limitations of conventional back propagation learning based DNM. ICRO-DNM is evaluated on closing price prediction for four stock index and net asset values of four mutual fund datasets and found efficient. The consequences of this attempt are: (1) Proposed ICRO shows robust parameter optimization ability for benchmark functions and DNM compared to basic CRO, particle swarm optimization (PSO), and genetic algorithm (GA); (2) The learning paradigm formed due to reasonable amalgamation of ICRO and DNM is pretty able to capture the underlying uncertainties coupled with financial data, produces more precise and steady predictions and significantly different from other forecasts.

show abstract

Section: Related Studiesmentioning

confidence: 99%

Intelligent Financial Forecasting With an Improved Chemical Reaction Optimization Algorithm Based Dendritic Neuron Model

2022

View full text Add to dashboard Cite

show abstract

“…Prediction and/or classification tasks are frequently found in many scientific and engineering fields with a large amount of potential artificial intelligence related techniques. The specific topics covered are rather diverse, including weather forecasts [21], plane flight time deviation [22], distributed networks [23], and many others [24][25][26]. One frequently used set of techniques are artificial neural networks.…”

Section: Forecasting and Classification Modelsmentioning

confidence: 99%

Alzheimer Identification through DNA Methylation and Artificial Intelligence Techniques

Perez

Caballero-Villarraso

2021

Mathematics

Self Cite

View full text Add to dashboard Cite

A nonlinear approach to identifying combinations of CpGs DNA methylation data, as biomarkers for Alzheimer (AD) disease, is presented in this paper. It will be shown that the presented algorithm can substantially reduce the amount of CpGs used while generating forecasts that are more accurate than using all the CpGs available. It is assumed that the process, in principle, can be non-linear; hence, a non-linear approach might be more appropriate. The proposed algorithm selects which CpGs to use as input data in a classification problem that tries to distinguish between patients suffering from AD and healthy control individuals. This type of classification problem is suitable for techniques, such as support vector machines. The algorithm was used both at a single dataset level, as well as using multiple datasets. Developing robust algorithms for multi-datasets is challenging, due to the impact that small differences in laboratory procedures have in the obtained data. The approach that was followed in the paper can be expanded to multiple datasets, allowing for a gradual more granular understanding of the underlying process. A 92% successful classification rate was obtained, using the proposed method, which is a higher value than the result obtained using all the CpGs available. This is likely due to the reduction in the dimensionality of the data obtained by the algorithm that, in turn, helps to reduce the risk of reaching a local minima.

show abstract

“…From the time and scale of the trend, it can be divided into: daily movement, secondary movement and mainstream trend. So the stock price is predictable [7][8].…”

Section: Predictability Of the Securities Marketmentioning

confidence: 99%

Prediction Method and Optimization of Stock Trend Based on Neural Network

2021

View full text Add to dashboard Cite

The stock market is characterized by the coexistence of high risks. In order to seek benefits and avoid risks, people do not hesitate to explore its laws and seek the best forecasting methods and methods. The purpose of this paper is to study the stock trend prediction method and optimization based on neural network. The predictability of the securities market is expounded. On the basis of the prediction model based on the historical stock transaction data, a stock prediction model integrating news and investor sentiment is constructed. The advantage of linear and complex time series forecasting problems, the stock training data is used as the input data of the improved CLSTM and BiLSTM deep neural network, and the stock trend forecasting model is trained. Continuously optimize and improve the model, select appropriate parameters to obtain a good stock trend prediction model. By continuously revising the LSTM level, the accuracy of the stock trend prediction model is significantly improved.

show abstract

Stock Forecasting Using Local Data

Cited by 15 publications

References 42 publications

Intelligent Financial Forecasting With an Improved Chemical Reaction Optimization Algorithm Based Dendritic Neuron Model

Intelligent Financial Forecasting With an Improved Chemical Reaction Optimization Algorithm Based Dendritic Neuron Model

Alzheimer Identification through DNA Methylation and Artificial Intelligence Techniques

Prediction Method and Optimization of Stock Trend Based on Neural Network

Contact Info

Product

Resources

About