Hybrid Quantum-Classical Neural Network (HQCNN) for Crypto Price Forecasting

digitalData = {"site":{"ip":"196.51.146.11","environment":"LIVE","website":"onlinelibrary.wiley.com","websiteCode":"pericles","serverDate":"2022-10-02"},"identities":[{"type":"SmartGroupUser","uuid":"b238ab85-6bb4-49e4-84ca-a0d9f3ee5a35"},{"type":"SmartGroupUser","uuid":"eabce89b-65c2-4a09-ab7c-bd2b3fea2f10"},{"type":"RegionUser","uuid":"3c5060a1-6620-438f-862b-902a4d4cd4ac"},{"type":"BasicGroup","uuid":"e623deab-c83b-41e3-84a2-d9f1d46c5f17"},{"type":"RegionUser","uuid":"79ad3272-fe6f-4ef7-b463-5204f4509918"},{"type":"SmartGroupUser","uuid":"a86d27d2-6bcd-4b94-bca5-fe16f4882855"},{"type":"SmartGroupUser","uuid":"da88d808-3f7b-4bde-9210-10862b6d121b"},{"type":"SmartGroupUser","uuid":"8c72a733-117a-4bc9-a682-13475f13e6a3"},{"type":"SmartGroupUser","uuid":"756fc162-8722-461f-a077-ebffec4f4fae"},{"type":"SmartGroupUser","uuid":"cf9c85b9-7f3b-4309-aeaa-c41669cabe3b"},{"type":"SmartGroupUser","uuid":"db8b8d10-1aed-4809-b3ee-70938c569014"},{"type":"SmartGroupUser","uuid":"cdce41c2-a8de-4a64-a4d9-a85caaaa2314"},{"type":"InstitutionUser","uuid":"dd40e26c-3e96-4e9f-9382-5d13a2a401f3","customerRecords":[{"customerDomain":"ALM-CU","customerNumber":"EAL57933332"}]},{"type":"SmartGroupUser","uuid":"7a2a316d-5a37-4ccd-afdb-737bfb863a48"},{"type":"SmartGroupUser","uuid":"39f911ce-44b6-4dba-9978-8b2e947a8eb0"},{"type":"SmartGroupUser","uuid":"97587ffd-dcb4-4cb5-9986-a8ce611532bd"},{"type":"SmartGroupUser","uuid":"a46a154d-979f-41ba-b459-fd34df5c071f"},{"type":"SmartGroupUser","uuid":"b7acda8c-ce18-451b-a68d-e14b72f9b5b8"},{"type":"SmartGroupUser","uuid":"055a2bdd-861f-4fa7-bd49-9773db7e0e3e"},{"type":"BasicGroup","uuid":"197fe835-5b5f-4f4e-b8e9-a74d68491a84"},{"type":"SmartGroupUser","uuid":"b2dbfe27-ecae-4bb6-bb0d-d75e5caa3762"},{"type":"SmartGroupUser","uuid":"1a6297fb-6679-4882-a4c2-fdd124624edf"},{"type":"SmartGroupUser","uuid":"8032f17e-5050-4fb4-9c40-e1f6875d5c2b"},{"type":"SmartGroupUser","uuid":"ab743440-628f-4964-9216-9983ad3beadf"},{"type":"SmartGroupUser","uuid":"a4187e00-f28e-4754-9a0f-1eb306fb0679"}],"publication":{"item":{"title":"Forecasting electricity price and demand using a hybrid approach based on wavelet transform, ARIMA and neural networks","doi":"10.1002/er.3067","access":"no","earliestDate":"2013-07-01","formats":["Abstract","Cited by","First page image","Full text","PDF","References"],"topics":[{"taxonomyUri":"global-subject-codes","topicLabel":"MECHANICAL ENGINEERING","topicUri":"me"},{"taxonomyUri":"global-subject-codes","topicLabel":"Materials for Energy Systems","topicUri":"msl0"},{"taxonomyUri":"global-subject-codes","topicLabel":"Hydrogen, Batteries & Fuel Cells","topicUri":"eg32"},{"taxonomyUri":"global-subject-codes","topicLabel":"General Energy","topicUri":"eg00"},{"taxonomyUri":"global-subject-codes","topicLabel":"Solar Energy & Photovoltaics","topicUri":"eg34"},{"taxonomyUri":"global-subject-codes","topicLabel":"Energy Reduction","topicUri":"ce58"},{"taxonomyUri":"global-subject-codes","topicLabel":"Renewable Energy","topicUri":"eg30"},{"taxonomyUri":"global-subject-codes","topicLabel":"General & Introductory Electrical & Electronics Engineering","topicUri":"ee00"},{"taxonomyUri":"global-subject-codes","topicLabel":"General & Introductory Mechanical Engineering","topicUri":"me00"},{"taxonomyUri":"global-subject-codes","topicLabel":"Constructional Physics","topicUri":"ce50"},{"taxonomyUri":"global-subject-codes","topicLabel":"MATERIALS SCIENCE","topicUri":"ms"},{"taxonomyUri":"global-subject-codes","topicLabel":"ENERGY","topicUri":"eg"},{"taxonomyUri":"global-subject-codes","topicLabel":"CIVIL ENGINEERING & CONSTRUCTION","topicUri":"ce"},{"taxonomyUri":"global-subject-codes","topicLabel":"ELECTRICAL & ELECTRONICS ENGINEERING","topicUri":"ee"}]},"group":{"title":"","doi":"10.1002/er.v38.5","access":"no","volume":"38","issue":"5","coverDate":"2014-04-01","topics":[{"taxonomyUri":"global-subject-codes","topicLabel":"MECHANICAL ENGINEERING","topicUri":"me"},{"taxonomyUri":"global-subject-codes","topicLabel":"Materials for Energy Systems","topicUri":"msl0"},{"taxonomyUri":"global-subject-codes","topicLabel":"Hydrogen, Batteries & Fuel Cells","topicUri":"eg32"},{"taxonomyUri":"global-subject-codes","topicLabel":"General Energy","topicUri":"eg00"},{"taxonomyUri":"global-subject-codes","topicLabel":"Solar Energy & Photovoltaics","topicUri":"eg34"},{"taxonomyUri":"global-subject-codes","topicLabel":"Energy Reduction","topicUri":"ce58"},{"taxonomyUri":"global-subject-codes","topicLabel":"Renewable Energy","topicUri":"eg30"},{"taxonomyUri":"global-subject-codes","topicLabel":"General & Introductory Electrical & Electronics Engineering","topicUri":"ee00"},{"taxonomyUri":"global-subject-codes","topicLabel":"General & Introductory Mechanical Engineering","topicUri":"me00"},{"taxonomyUri":"global-subject-codes","topicLabel":"Constructional Physics","topicUri":"ce50"},{"taxonomyUri":"global-subject-codes","topicLabel":"MATERIALS SCIENCE","topicUri":"ms"},{"taxonomyUri":"global-subject-codes","topicLabel":"ENERGY","topicUri":"eg"},{"taxonomyUri":"global-subject-codes","topicLabel":"CIVIL ENGINEERING & CONSTRUCTION","topicUri":"ce"},{"taxonomyUri":"global-subject-codes","topicLabel":"ELECTRICAL & ELECTRONICS ENGINEERING","topicUri":"ee"}]},"series":{"title":"International Journal of Energy Research","doi":"10.1002/(ISSN)1099-114X","access":"no","code":"1099114x","issn":"0363-907X","eissn":"1099-114X","topics":[{"taxonomyUri":"gpt-sitename","topicLabel":"wly.civ.eng.con_000034","topicUri":"wly.civ.eng.con_000034"},{"taxonomyUri":"global-subject-codes","topicLabel":"MECHANICAL ENGINEERING","topicUri":"me"},{"taxonomyUri":"global-subject-codes","topicLabel":"MATERIALS SCIENCE","topicUri":"ms"},{"taxonomyUri":"global-subject-codes","topicLabel":"Materials for Energy Systems","topicUri":"msl0"},{"taxonomyUri":"global-subject-codes","topicLabel":"Renewable Energy","topicUri":"eg30"},{"taxonomyUri":"global-subject-codes","topicLabel":"Constructional Physics","topicUri":"ce50"},{"taxonomyUri":"global-subject-codes","topicLabel":"General Energy","topicUri":"eg00"},{"taxonomyUri":"global-subject-codes","topicLabel":"General & Introductory Electrical & Electronics Engineering","topicUri":"ee00"},{"taxonomyUri":"global-subject-codes","topicLabel":"ENERGY","topicUri":"eg"},{"taxonomyUri":"global-subject-codes","topicLabel":"Energy Reduction","topicUri":"ce58"},{"taxonomyUri":"global-subject-codes","topicLabel":"CIVIL ENGINEERING & CONSTRUCTION","topicUri":"ce"},{"taxonomyUri":"global-subject-codes","topicLabel":"General & Introductory Mechanical Engineering","topicUri":"me00"},{"taxonomyUri":"global-subject-codes","topicLabel":"ELECTRICAL & ELECTRONICS ENGINEERING","topicUri":"ee"},{"taxonomyUri":"global-subject-codes","topicLabel":"Solar Energy & Photovoltaics","topicUri":"eg34"},{"taxonomyUri":"global-subject-codes","topicLabel":"Hydrogen, Batteries & Fuel Cells","topicUri":"eg32"},{"taxonomyUri":"gpt-zonename","topicLabel":"ij-energy-res_ER","topicUri":"ij-energy-res_er"}]}},"page":{"subPage":"Abstract","page":"Article/Chapter View","pageGroup":"Publication Pages","title":"title","errorCode":200}}

In this paper, an artificial neural network (ANN) based methodology is proposed to forecast electricity load and price. The performance of an ANN forecast model depends on appropriate input parameters. Parameter tuning of ANN is very important to increase the accuracy of electricity price and load prediction. This is done using mutual information and decision tree. After selecting best features for forecasting, these features are given to forecasting engine working on principles of recurrent neural network (RNN). For simulations, dataset is taken from national electricity market (NEM), Australia. Results show that the methodology has increased the accuracy of electricity load and price forecast. Whereas, the error rate of forecasting is lower than the other models for electricity load and price.

Previous studies proposed several bio-inspired algorithms for the optimization of Neural Network (NN) to avoid local minima and to improve accuracy and convergence speed. To advance the performance of NN, a new bio-inspired algorithm called Flower Pollination Algorithm (FPA) is used to optimize the weights and bias of NN due to its ability to explore very large search space and frequent chosen of similar solution. The FPA optimized NN (FPNN) was applied to build a model for the prediction of Dubai crude oil price unlike previous studies that mainly focus on the West Texas Intermediate and Brent crude oil price benchmarks. Results suggested that the FPNN was found to improve the convergence speed and accuracy of the cuckoo search algorithm and artificial bee colony optimized NN in the prediction of Dubai crude oil price. The Middle East region that produces a significant amount of crude oil relies on the Dubai crude oil price to benchmark prices for exporting crude oil to Asian countries. Our model could be of help to the Middle East region for monitoring possible fluctuations in the Dubai crude oil market price so as to take better decision related to international crude oil price.

In a competitive electricity market, forecast of energy prices is a key information for the market participants. However, price signal usually has a complex behavior due to its nonlinearity, nonstationarity, and time variancy. In spite of all performed researches on this area in the recent years, there is still an essential need for more accurate and robust price forecast methods. In this paper, a combination of a feature selection technique and neural network (NN) is proposed for this purpose. The feature selection method is a modified version of the relief algorithm, proposed for the feature selection of price forecasting. Then, by means of the most relevant, explanatory and irredundant features, a neural network (NN) predicts the next values of the price signal. The adjustable parameters of the whole method are fine-tuned by a cross-validation technique. The proposed method is examined on PJM electricity market, forecasting day-ahead locational marginal prices (LMPs), and compared with some of the most recent price forecast methods especially some other popular and validated feature selection techniques. These comparisons indicate the validity and robustness of the proposed forecasting method.

House price forecasting with neural networks

Precise price forecasting can lessen the risk of participation in the deregulated electricity market. On account of a large amount of historical data, deep learning based methods can be a promising solution to achieve an accurate forecast. This study presents a deep neural network algorithm to estimate the probability density function of price, incorporating the prediction of wind speed and residential load as two other high volatile parameters. To this end, first, a combination of convolution neural network (CNN) and gated recurrent unit (GRU) is utilised to predict wind speed and residential load. Then, the results are integrated into historical price information to form the input dataset for price forecasting. The proposed price forecast procedure consists of CNN, GRU, and adaptive kernel density estimator (AKDE). AKDE is used as a numerical algorithm to capture probabilistic characterisation of real‐time and day‐ahead prices. Several deep and shallow networks and the proposed algorithm are implemented, and the results are compared. Furthermore, the effectiveness of AKDE in providing complete statistical information is verified through comparison with conventional and fixed smooth KDEs. In addition, the gradient boosting tree method is incorporated to verify the dependence of the price to the wind and the residential loads.

Thermal coal price forecasting via the neural network

In the new competitive electricity markets, accurate forecast of electricity prices is valuable for both producers and consumers. Due to the volatility of electricity price signal and limited available information, there is an essential need to accurate and robust forecasting methods for the price prediction. In this paper a data mining technique, mutual information, is proposed for the feature selection of price forecasting. Then, by means of the selected features, a neural network (NN) predicts the next values of the price signal. The whole proposed method (MI+NN) is examined on the day-ahead electricity market of PJM. The obtained results are compared with the results of some other price forecast methods and especially the other feature selection techniques. This comparison indicates the validity of the developed approach.

The market fish price is an important factor that affects the income of fishermen, so how to accurately analyze and predict the fish pricet o obtain huge profits has caught people's attention. As science advances, various price forecasting and analysis methods have come into being. How to build a prediction theories and models with relatively high success rate has been the study of many scholars over the years. With the development of artificial intelligence, neural networks have become an important tool of predicting and analyzing changes in market prices. Neural networks are important artificial intelligence technology, which have simple structures, but are able to solve complicated problems. They have strong applicability in predicting the mature index fluctuations in a short period. This paper considers some shortcomings and deficiencies the BP network prototype, which tries to use the wavelet Functions to replace the excitation function in the traditional BP algorithm on the basis of a network of neurons and then forms into WNN. We can verify the feasibility of WNN by perch price forecasts, and then this method is used in price forecasts of the three main fish of the Ulungur Lake Aquatic, to provide the basis for the aquatic base decision

Prediction of pricing and hedging errors for equity linked warrants with Gaussian process models☆

ESAENARX and DE-RELM: Novel schemes for big data predictive analytics of electricity load and price

Forecasting day-ahead electricity prices: A comparison of time series and neural network models taking external regressors into account

A new feature selection algorithm and composite neural network for electricity price forecasting

Bitcoin, the first decentralized cryptocurrency, has attracted significant attention from investors and researchers alike due to its volatile and unpredictable price movements. However, predicting the price of Bitcoin remains a challenging task. This paper presents a detailed literature review on previous studies that have attempted to predict the price of Bitcoin. It discusses the main drivers of Bitcoin prices, including its attractiveness, macroeconomic and financial factors with a particular focus on the use of Blockchain information. We apply time series to daily data for the period from 28/04/2013 to 28/01/2023. We used Python and TensorFlow library version 2.11.0 and propose a deep multimodal reinforcement learning policy combining Convolutional Neural Network (CNN) and Long ShortTerm Memory (LSTM) neural network for cryptocurrencies’ prices prediction. Also, this study attempts to predict the price of Bitcoin using a special type of deep neural networks, a Deep Autoencoders. Two results are worth noting: Autoencoders turns out to be the best method of predicting Bitcoin prices, and Bitcoin-specific Blockchain information is the most important variable in predicting Bitcoin prices. This study highlights the potential utility of incorporating Blockchain factors in price prediction models. Also, our findings show that sentiment indicator, Ethereum, XRP and Doge Coin prices, global currency ratio, macroeconomic factors, and Blockchain information of Ethereum did not contribute significantly toward predicting Bitcoin prices. These conclusions provide decision support for investors and a reference for the governments to design better regulatory policies.

Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method