Ping-Huan Kuo^1,2, Sing-Yan Chen¹, Her-Terng Yau^1,2,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1577-1596, 2023, DOI:10.32604/csse.2023.036292

Abstract Cars are regarded as an indispensable means of transportation in Taiwan. Several studies have indicated that the automotive industry has witnessed remarkable advances and that the market of used cars has rapidly expanded. In this study, a price prediction system for used BMW cars was developed. Nine parameters of used cars, including their model, registration year, and transmission style, were analyzed. The data obtained were then divided into three subsets. The first subset was used to compare the results of each algorithm. The predicted values produced by the two algorithms with the most satisfactory results were used as the input… More >

Ahmed Salah^1,2, Mahmoud Bekhit³, Esraa Eldesouky^4,5, Ahmed Ali^4,6,*, Ahmed Fathalla⁷

Computer Systems Science and Engineering, Vol.46, No.1, pp. 445-460, 2023, DOI:10.32604/csse.2023.035254

Abstract The price prediction task is a well-studied problem due to its impact on the business domain. There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change, but there is very limited work to study the price prediction of seasonal goods (e.g., Christmas gifts). Seasonal items’ prices have different patterns than normal items; this can be linked to the offers and discounted prices of seasonal items. This lack of research studies motivates the current work to investigate the problem of seasonal items’ prices as a time series task.… More >

J. Faritha Banu^1,*, S. B. Rajeshwari², Jagadish S. Kallimani², S. Vasanthi³, Ahmed Mateen Buttar⁴, M. Sangeetha⁵, Sanjay Bhargava⁶

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1393-1405, 2022, DOI:10.32604/iasc.2022.024176

Abstract The stock market is an important domain in which the investors are focused to, therefore accurate prediction of stock market trends remains a hot research area among business-people and researchers. Because of the non-stationary features of the stock market, the stock price prediction is considered a challenging task and is affected by several factors. Anticipating stock market trends is a difficult endeavor that requires a lot of attention, because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, noisy, and chaotic data, stock market prediction is a huge difficulty, and as… More >

Singamaneni Kranthi Kumar^1,*, Alhassan Alolo Abdul-Rasheed Akeji², Tiruvedula Mithun³, M. Ambika⁴, L. Jabasheela⁵, Ranjan Walia⁶, U. Sakthi⁷

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1217-1227, 2022, DOI:10.32604/iasc.2022.024311

Abstract In recent times, stock price prediction helps to determine the future stock prices of any financial exchange. Accurate forecasting of stock prices can result in huge profits to the investors. The prediction of stock market is a tedious process which involves different factors such as politics, economic growth, interest rate, etc. The recent development of social networking sites enables the investors to discuss the stock market details such as profit, future stock prices, etc. The proper identification of sentiments posted by the investors in social media can be utilized for predicting the upcoming stock prices. With this motivation, this paper… More >

Syed H. Hasan¹, Syeda Huyam Hasan², Mohammed Salih Ahmed³, Syed Hamid Hasan^4,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1051-1063, 2022, DOI:10.32604/cmc.2022.020823

Abstract In recent years, cryptocurrency has become gradually more significant in economic regions worldwide. In cryptocurrencies, records are stored using a cryptographic algorithm. The main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social media. Twitter is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media channels. Therefore, this work focuses on extracting Tweets and gathering data from different sources to classify them into positive, negative, and neutral categories, and further examining the correlations between cryptocurrency movements… More >

Mohamed Ali Mohamed, Ibrahim Mahmoud El-Henawy, Ahmad Salah^*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3473-3489, 2022, DOI:10.32604/cmc.2022.020782

Abstract Price prediction of goods is a vital point of research due to how common e-commerce platforms are. There are several efforts conducted to forecast the price of items using classic machine learning algorithms and statistical models. These models can predict prices of various financial instruments, e.g., gold, oil, cryptocurrencies, stocks, and second-hand items. Despite these efforts, the literature has no model for predicting the prices of seasonal goods (e.g., Christmas gifts). In this context, we framed the task of seasonal goods price prediction as a regression problem. First, we utilized a real online trailer dataset of Christmas gifts and then… More >

Naeem Ahmed Mahoto¹, Rabia Iftikhar¹, Asadullah Shaikh^2,*, Yousef Asiri², Abdullah Alghamdi², Khairan Rajab^2,3

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 147-159, 2021, DOI:10.32604/iasc.2021.018944

Abstract The price of a product plays a vital role in its market share. Customers usually buy a product when it fits their needs and budget. Therefore, it is an essential area in the business to make decisions about prices for each product. The major portion of the business profit is directly connected with the percentage of the sale, which relies on certain factors of customers including customers’ behavior and market competitors. It has been observed in the past that machine learning algorithms have made the decision-making process more effective and profitable in businesses. The fusion of machine learning with business… More >

Ajla Kulaglic^1,*, Burak Berk Ustundag²

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3577-3593, 2021, DOI:10.32604/cmc.2021.014768

Abstract Machine Learning (ML) algorithms have been widely used for financial time series prediction and trading through bots. In this work, we propose a Predictive Error Compensated Wavelet Neural Network (PEC-WNN) ML model that improves the prediction of next day closing prices. In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs. An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence. The performance of the proposed model is evaluated using six different stock data samples… More >

Anh H. Vo¹, Trang Nguyen², Tuong Le^1,3,*

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1307-1317, 2020, DOI:10.32604/iasc.2020.013189

Abstract Brent oil price fluctuates continuously causing instability in the economy. Therefore, it is essential to accurately predict the trend of oil prices, as it helps to improve profits for investors and benefits the community at large. Oil prices usually fluctuate over time as a time series and as such several sequence-based models can be used to predict them. Hence, this study proposes an efficient model named BOP-BL based on Bidirectional Long Short-Term Memory (Bi-LSTM) for oil price prediction. The proposed framework consists of two modules as follows: The first module has three Bi-LSTM layers which help learning useful information features… More >

Weijie Chen¹, Guo Feng¹, Chao Zhang¹, Pingzeng Liu^1,*, Wanming Ren², Ning Cao³, Jianrui Ding⁴

CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 229-248, 2019, DOI:10.32604/cmc.2019.03743

Abstract In order to effectively solve the problems which affect the stable and healthy development of garlic industry, such as the uncertainty of the planting scale and production data, the influence factors of price fluctuation is difficult to be accurately analyzed, the difficult to predict the trend of price change, the uncertainty of the market concentration, and the difficulty of the short-term price prediction etc. the big data platform of the garlic industry chain has been developed. Combined with a variety of data acquisition technology, the information collection of influencing factors for garlic industry chain is realized. Based on the construction… More >