Cryptocurrency Price Prediction with Neural Networks of LSTM and Bayesian Optimization

Pour, Ehsan Sadeghi; Jafari, Hossein; Lashgari, Ali; Rabiee, Elaheh; Ahmadisharaf, Amin

doi:10.24018/ejbmr.2022.7.2.1307

Cited by 16 publications

(8 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given to this point that huge impact of the advertising text on human behaviour and market, it is vital to note the common values of audiences and should always align itself with different sub-cultures. Here, unconventional advertising can be utilised to promote the level of culture (a type of culture for all) (Khoshnava & Naseri 2017;Pour et al 2022). Advertising in digital space has become one of the fastest growing practices for marketers to connect with consumers from different cultures (Bozorgkhou et al 2019;Chetverikova 2020;James et al 2017):…”

Section: Islamic Digital Adsmentioning

confidence: 99%

Culture and religion creolisation impact on digital advertisement of Muslim users of Instagram

Mirvaisi¹,

Kaffashpoor

2023

HTS Teologiese Studies / Theological Studies

View full text Add to dashboard Cite

Over the last decade, digitalisation has been a subject of increasing attention among scholars and practitioners. The effect of culture and religion on advertisements, consumerism and marketing, is deniable. The main goal of this research is to present a comprehensive conceptual model based on cultural and religious diversity in digital marketing. This research mainly includes introduction creolisation concept and the elements (religion, music, clothes and custom) as the most important factor in digital advertisement and branding. In this regard, this study examines Instagram consumer products users from five different subcultures in Iran (Lor, Kurdish, Baluch, Arab and Azeri) and considers the impact of creolisation elements on digital advertising, brand identity and brand globalisation. Data were collected by sending 1200 questionnaire via email to subcultures and then analysed using LISREL software.Contribution: The results demonstrated that Islamic religion between participants affects their brand loyalty, therefore the Islamic worth in advertising is necessary. It was suggested that creolisation elements significantly affect digital advertisement, brand identity and brand globalisation. Consequently, all hypotheses were accepted. On the other hand, the impact of brand identity and digital advertisement on the brand globalisation was also confirmed. Discussion and managerial implications concerning creolisation were explained.

show abstract

Section: Islamic Digital Adsmentioning

confidence: 99%

Culture and religion creolisation impact on digital advertisement of Muslim users of Instagram

Mirvaisi¹,

Kaffashpoor

2023

HTS Teologiese Studies / Theological Studies

View full text Add to dashboard Cite

show abstract

“…Pour et al [40] proposed a hybrid model for Bitcoin price prediction that uses Long Short-Term Memory and Bayesian Optimization. Their model was validated using MSE, RMSE, and NRMSE.…”

Section: Related Studiesmentioning

confidence: 99%

“…The target is the GS approach to find the best combination between the sequence length and day shift value. The sequence length range is [10,20,30,40,50] and the days shift range is [1, 2, 3, 4, 5].…”

Section: Classification and Optimization Phasementioning

confidence: 99%

DLCP2F: a DL-based cryptocurrency price prediction framework

Aljadani

2022

Discov Artif Intell

View full text Add to dashboard Cite

Cryptocurrencies are distributed digital currencies that have emerged as a consequence of financial technology advancement. In 2017, cryptocurrencies have shown a huge rise in their market capitalization and popularity. They are now employed in today’s financial systems as individual investors, corporate firms, and big institutions are heavily investing in them. However, this industry is less stable than traditional currency markets. It can be affected by several legal, sentimental, and technical factors, so it is highly volatile, dynamic, uncertain, and unpredictable, hence, accurate forecasting is essential. Recently, cryptocurrency price prediction becomes a trending research topic globally. Various machine and deep learning algorithms, e.g., Neural Networks (NN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) were utilized to analyze the factors influencing the prices of the cryptocurrencies and accordingly predict them. This paper suggests a five-phase framework for cryptocurrency price prediction based on two state-of-the-art deep learning architectures (i.e., BiLSTM and GRU). The current study uses three public real-time cryptocurrency datasets from “Yahoo Finance”. Bidirectional Long Short-Term Memory and Gated Recurrent Unit deep learning-based algorithms are used to forecast the prices of three popular cryptocurrencies (i.e., Bitcoin, Ethereum, and Cardano). The Grid Search approach is used for the hyperparameters optimization processes. Results indicate that GRU outperformed the BiLSTM algorithm for Bitcoin, Ethereum, and Cardano, respectively. The lowest RMSE for the GRU model was found to be 0.01711, 0.02662, and 0.00852 for Bitcoin, Ethereum, and Cardano, respectively. Experimental results proved the significant performance of the proposed framework that achieves the minimum MSE and RMSE values.

show abstract

“…In addition, good forecasting models can be of great help for risk management, portfolio optimization as well as long-term trading plans [13]. To accomplish the goal, this research uses a multi-dimensional methodology which includes data collection, feature engineering, model development, and performance assessment [3]. A dataset that is diverse which comprises of historical cryptocurrency prices from various exchanges, transaction volumes, market sentiment indicators, and fundamental factors is incorporated [10].…”

Section: Introductionmentioning

confidence: 99%

“…In this study, machine learning algorithms include widely used techniques, for instance, linear regression, support vector machines, random forest, and deep learning networks [1]. The algorithms differ in every aspect and the performance evaluation is thorough enough to reveal the best performing method for cryptocurrency price prediction [3]. Feature 1* Associate Professor, Brainware University, aichjayanta9@gmail.com, 0000-0003-3187-0933 2 Bharati Vidyapeeth's College of Engineering for Women, Pune, Maharashtra, India.…”

Section: Introductionmentioning

confidence: 99%

Performance Scrutiny of Price Prediction on Blockchain Technology Using Machine Learning

Jayanta Aich

2024

jes

View full text Add to dashboard Cite

Objective: This study, aiming to assess the accurateness of machine learning algorithms for the prediction of cryptocurrency prices, using blockchain technology, was conducted to attest to its effectiveness. The main goal is to evaluate the precision, solidity, and prospects of such models under variable and fragmented trading premises. Methodology: Dataset containing historical crypto prices, transaction volumes, emotions of investors, etc. is formed from different and various sources. Different machine learning approaches including linear regression, support vector machine, random forest, and deep learning networks will be used to construct the prediction model. The split of the dataset into training and testing subsets is performed to better evaluate and estimate the models' performance. Feature engineering techniques and parameter tuning are included in the process of making models more precise. Models are the ones which are the trained using historical data and then later tested out on the unseen data for the purpose of accurate results and generalization power. Results and Discussion: From the experiment it is evident that a shift of different machine learning algorithms exhibits differing outcomes. While certain models can yield admirable results in forecasting crypto prices in shorter terms, however, there are some models that are not capable of capturing the complicated impurities. Factors like data quality, function selection and model complexity are at the core of predictive power. The learning networks demonstrated the capability to reproduce nonlinearity and temporal relatedness in cryptocurrency value data. Conclusion: Cryptocurrency forecasting through blockchain technology involves the use of machine learning techniques which has a lot of potential. The success of these models also depends on the quality of the data, feature refinement and model choice. Research progress should focus on refining currently existing models, considering new data inputs, and introduction of advanced machine learning methods which should be able to increase forecast accuracy in rapidly changing cryptocurrency markets.

show abstract

Cryptocurrency Price Prediction with Neural Networks of LSTM and Bayesian Optimization

Cited by 16 publications

References 3 publications

Culture and religion creolisation impact on digital advertisement of Muslim users of Instagram

Culture and religion creolisation impact on digital advertisement of Muslim users of Instagram

DLCP2F: a DL-based cryptocurrency price prediction framework

Performance Scrutiny of Price Prediction on Blockchain Technology Using Machine Learning

Contact Info

Product

Resources

About