Price Prediction Research Articles

Second-hand car price prediction based on multiple linear regression and random forest

The second-hand car market is a hot topic. Buying a second-hand car has advantages in price and many other aspects. Therefore, it is important to establish a good price prediction model. This paper will explore the factors that affect the price of second-hand cars. After analyzing and learning many kinds of literature, this paper establishes a multiple linear regression model and a random forest model and makes a comparative analysis of the model effect. The sum of the square error and R-square value of the random forest are better than the multiple linear regression model. Among the factors affecting the price of second-hand cars, the year of production has the greatest impact on the price, which shows that the age of the year is an important factor in determining the price of second-hand cars. The next most important factor is the number of kilometers traveled, followed by fuel type and transmission type-finally, engine displacement, number of transfers and number of seats. The random forest model established in this paper has better application value to price prediction.

A Research of Fine-Tuning CNN-LSTM Model for Gold Price Prediction

Gold price and foreign exchange price prediction are usually regarded as serialized data with strong time correlation. It is precisely because of this feature that the performance of traditional convolutional neural networks is often not as good as that of recurrent neural networks. This paper fine-tunes the CNN-LSTM model in two ways. The first is to simplify the input data, convert the time series into binary data, and change the model task from predicting prices to predicting price fluctuations. The second is to add a dropout layer and use the dropout layer to improve the model's overfitting problem. The results demonstrate that the improved model outperforms the first model, with prediction accuracy rising from 54% to 57%. Furthermore, the refined model is not limited to processing the gold price series; it can also be applied to other time series, and when paired with other models, it could yield even better outcomes.

Oil-price forecasting based on ARIMA, exponential smoothing, and autoregressive neural network models

Oil prices have an impact on the global economy. Demand shocks, such as those caused by the recent COVID-19 epidemic, or supply shocks caused by production reductions by major global producers, result in sudden price changes. Oil industry has been collecting price data since the late 19th century. A time series of this magnitude allows for consistent analysis of price behavior forecasts following demand or supply shocks. Financial time series are sensitive to exogenous shocks. From this perspective, this work presents a comparative analysis of predictive crude oil prices scenarios, obtained from a historical series of average annual prices. Two approaches were used: first, a combination of classic strategies based on exponential smoothing, and ARIMA models. Second, an autoregressive neural network model. Both approaches are complementary when used for long-term forecasting of oil prices and show good response to volatile data. Therefore, we are able to present an alternative data analysis, in a field where there is a great amount of relevant historical series, using probabilistic and non-linear models in order to observe predictions and make more effective decisions.

Prediction of second-hand sailboat prices based on multivariate linear interpolation methods

According to the requirements of the topic, our team has established a mathematical model based on a large amount of relevant data and conducted a quantitative analysis of factors such as the price of sailboats and their length, types, materials, etc. In the process of data analysis and model building, we mainly use interpolation method and error variance analysis, and display these results through charts and figures. In addition, we have used the obtained model to analyze and estimate the price of sailboats in Hong Kong.

Prediction of second-hand sailboat prices based on multivariate linear interpolation methods

According to the requirements of the topic, our team has established a mathematical model based on a large amount of relevant data and conducted a quantitative analysis of factors such as the price of sailboats and their length, types, materials, etc. In the process of data analysis and model building, we mainly use interpolation method and error variance analysis, and display these results through charts and figures. In addition, we have used the obtained model to analyze and estimate the price of sailboats in Hong Kong.

Research on Influencing Factors and Trend Prediction of New Energy Second-hand Car Prices

With the advancement of technology, due to their lower cost and high performance than traditional fuel cars, new energy vehicles have been gradually occupying an increasingly important position in the economy and society for years. However, in addition to the continuous expansion of the industry, lots of consumers and dealers have turned their attention to the post production issues of new energy vehicles - the second-hand market. Its widely believed by the scholars that the resale value of new energy vehicles is influenced by a great many factors, mainly including the using time, mileage, policies and so on. This article used the Tesla model 3 as an example and established interpretative structural modeling (ISM) and ordinary least squares (OLS) models, in order to do factor analysis and trend prediction on the resale value of new energy vehicles. After containing quantities of results, the research ultimately found out the specific negative correlation between two major elements and the second-hand price of the new energy cars, together with simple point predictions through OLS models. In this condition, it is feasible for the dealer and costumers of the new energy vehicles market to get a standard in second-hand prices, while the theoretical field could also be partly extended in the aspect of mathematical statistics and economic analysis.

Research on gold price prediction based on ARIMA model

Gold is one of the most prevalent currencies in the world and its price has a very strong influence in the global financial markets. Gold has safe-haven properties, which can have a significant impact on its demand and price, especially in times of social unrest or financial crisis. Now, the demand for gold by investors has increased dramatically. Therefore, being able to accurately predict the direction of the gold price can help investors to effectively develop investment strategies and risk management measures. The overall objective of this study is to forecast the price of gold futures for the next six months. In this study, the Kaggle website was searched to find the price of gold from 2020 to 2024 and finally the CLOSE price was chosen as the final predicted price. This paper uses the ARIMA model for gold price forecasting. By comparing the RSME size of each model, ARIMA (1, 1, 2) is finally chosen. From the prediction results the price of gold remains stable in the first half of the year and then increases significantly. From the results of the residual test, there is no autocorrelation, and then it is white noise.

Metal Futures Prices Prediction Based on LSTM-GRU Hybrid Model

Accurately predicting the prices of metal futures such as aluminum, copper, lead, nickel, tin, and zinc is crucial for investors, manufacturers, and policymakers due to their significant impact on economic activities and industrial processes. This paper presents a comprehensive study on the prediction of futures prices using a hybrid model combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks. The research utilizes historical data from 2014 to 2024, including daily open, high, low, close prices, trading volumes, and return. Data preprocessing involved interpolation for missing values and normalization using MinMaxScaler. The models performance was evaluated with various accuracy metrics, such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE) ect, showing high accuracy for lead, tin, and zinc futures in both train and test datasets. However, the model performed well only on the train data for aluminum, copper, and nickel, indicating the need for more iterations, additional factors, or the consideration of unexpected events. The results highlight the efficacy of the LSTM-GRU hybrid model in futures price prediction, with significant implications for informed investment decisions. Future research should consider cross-market analysis and additional factors to enhance model accuracy further.

Prediksi Harga Crypto dengan Algoritma Jaringan Saraf Tiruan

Cryptocurrency has emerged as a crucial element in the global financial market, prompting investor interest in predicting prices for informed investment decision-making. However, the highly volatile nature of cryptocurrency prices has made trading in the crypto market speculative and laden with significant risks. Therefore, this study aims to apply Artificial Neural Networks (ANNs) using the Long Short-Term Memory (LSTM) algorithm to forecast cryptocurrency prices. This method leverages the ANN's ability to recognize complex patterns and trends in historical cryptocurrency price data. The research findings demonstrate significant accuracy levels for three types of crypto: BTC (86.86%), BNB (96.8%), and Doge (97%). Evaluation was conducted using the k-fold cross-validation method with k=5, where data was divided into five equally sized groups. Accuracy was computed by comparing actual prices with those predicted by the LSTM model. This evaluation approach provides robust insights into the LSTM model's effectiveness in predicting cryptocurrency prices, considering significant data variations. The predicted values from this study are BTC at $4306, BNB at $58.7, and Dogecoin at $0.037. These implications underscore the critical role of the number of epochs in influencing the performance and accuracy of cryptocurrency price predictions. Thus, this research is poised to offer deeper insights into cryptocurrency market behavior and provide practical guidance for investors to make more informed and measured investment decisions.

Review of machine learning with sentimental analysis method for cross-model stock price prediction

Stock market prediction has been a popular area of research for years. Machine learning, as a fast-developing popular algorithm, is applied to stock market prediction by many previous researchers to better solve the time series involving problems over the changing prices. Different from traditional machine learning algorithms that focus on stock prices only, this paper gives a brief description and review of stock price predictions models that contain sentimental analysis over the recent paper works. Different from the prices in number format, sentimental analysis is more based on textual information extraction and mining, converting them into usable input to pass to a prediction model. This extends the prediction model's takeable input domain and strengthens the accuracy. To better classify the differences between the models, discussion and introduction are given based on different model types about whether they are traditional type or deep neural network embedded. Even though traditional types of models are more popular for sentimental analysis and neural networks perform better in prediction tasks, traditional methods are relatively easy to build or train with more explainability, compared to deep learning models suitable for larger data sets.

Harnessing machine learning for enhanced quantitative financial strategies: Applications and case studies

Machine learning algorithms have long been an essential tool for quantitative finance, as they play an important role in the asset pricing, portfolio optimisation and financial risk management. This work discusses how to apply Random Forest, Support Vector Machine (SVM), and Neural Networks to further develop and enhance the quantitative investment research. With automatic feature selection, reconstruction of high-dimensional data and resist against over-fitting, Random Forest has been an efficient ensemble approach with remarkable prediction performance. For this reason, Random Forest is leveraged for the stock price prediction. SVM, particularly known for its robustness in high-dimensional classification task, has a non-linear optimisation ability that can be employed to capture the inherent non-linear dependencies in options prices. Neural Networks, as a learning approach capable of discovering complex relationships, is employed for the portfolio optimisation, with a promise for further improving the risk-adjusted portfolio return. The work is conducted through practical case studies to simulate how Random Forest, SVM and Neural Networks enable us to further improve the financial performance, and to illustrate its advantage, disadvantage and practical application in quantitative finance.

Trading Momentum in the U.S. Crude Oil Futures Market

This paper investigates if the Rate of Change (ROC), as a popular measure of momentum, can serve as a reliable technical analysis indicator to improve stock price prediction for U.S. West Texas Intermediate (WTI) crude oil futures market. The methodology centers on the application of the ROC and/or Moving Average (MA) price crossover/cross under strategies as a trading system. End of month futures prices of West Texas Intermediate (WTI) crude oil prices are collected for the period 28th May 2004 – 30th April 2024. The performance of the trading system is measured using both the Sharpe and Sortino ratios, thereby adjusting for total and downside risks. The model is also benchmarked against the naïve buy-and-hold strategy. Overall findings suggest a ROC based on 14-month periods outperform other momentum-based indicators, including when combined with price-crossover moving average strategies, and the naïve buy-and-hold strategy. After adjusting for the negative returns, the downside risk or semi-deviation amounted to 8.5%, and a Sortino value of 4.58. The Sortino value is however biased due to the 295% return witnessed in 2009. Findings have some vital policy implications for regulatory bodies and traders in the WTI crude oil energy futures market.

Intelligent crude oil price probability forecasting: Deep learning models and industry applications

The crude oil price has been subject to periodic fluctuations because of seasonal changes in industrial demand and supply, weather, natural disasters and global political unrest. An accurate forecast of crude oil prices is of utmost importance for decision makers and industry players in the energy sector. Despite this, the volatility of crude oil prices contributes to the uncertainty of the energy industry, which was particularly challenging following the recent global spread of the COVID-19 epidemic and the Russia–Ukraine conflict. This paper proposes a hybrid deep learning (DL) modelling framework to deal with the volatility of crude oil prices, applying ensemble empirical mode decomposition (EEMD), convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) integrated with quantile regression (QR); named EEMD-CNN-BiLSTM-QR. Two real-world datasets on crude oil prices from the West Texas Intermediate and Brent Crude Oil markets were employed to validate the EEMD-CNN-BiLSTM-QR hybrid modelling framework. Given that the probability density forecast can capture uncertainty, an in-depth analysis was carried out and prediction accuracy calculated. The findings of this study demonstrate that the proposed EEMD-CNN-BiLSTM-QR DL modelling framework, which uses the probability density forecast method, is superior to other tested models in terms of its ability to forecast crude oil prices. The novelty of this study stems mainly from its use of QR, which allows for the description of the conditional distribution of predicted variables and the extraction of more uncertain information for probability density forecasts.

Dung beetle optimization with deep learning approach for solving inverse problems in predicting financial futures

Leveraging deep learning (DL) to inverse problems has proven transformative in predicting financial futures, mainly in stock price prediction. In terms of financial markets, where predicting stock price is a challenging inverse problem, DL methods like long short-term memory networks (LSTM) and recurrent neural networks (RNN) demonstrate notable prowess. These techniques successfully capture complex patterns and dependencies in historical stock price information, learning to discern subtle correlations and fundamental market trends. DL facilitates the extraction of nuanced features by leveraging large datasets and sophisticated techniques, enabling accurate prediction of future stock prices. This intersection of advanced ML approaches and financial predicting signifies a paradigm shift in leveraging technology to navigate the complexity of the financial market and improves decision-making for traders and investors. This manuscript introduces a Dung Beetle Optimization with a Deep Learning Approach for Solving Inverse Problems in Predicting Financial Futures (DBODL-SIPPFF) technique. The DBODL-SIPPFF technique resolves the inverse problem and predicts the stock prices adequately. In addition, the DBODL-SIPPFF technique exploits data consistency (DC) optimizer to solve inverse problems. Primarily, linear scaling normalization (LSN) is applied for data normalization. The honey badger algorithm (HBA) is utilized for the feature selection. Moreover, convolutional long short-term memory (ConvLSTM) is used for stock price prediction. Lastly, the DBO technique is utilized for the optimum hyperparameter selection of the ConvLSTM model. The empirical assessment of the DBODL-SIPPFF technique takes place under diverse stock datasets. The obtained values stated that the DBODL-SIPPFF technique performs optimally over compared methods.

Stock Price Forecasting using N-Beats Deep Learning Architecture

Stock prices present unique forecasting challenges due to factors such as market volatility, investor sentiment, and economic indicators, which contribute to significant fluctuations in time series data. This paper addresses these complexities by applying Deep Learning (DL) models to predict stock prices, with a particular focus on the S&P 500 index. Although DL models have shown remarkable success in fields like image processing and natural language processing, they require specialized architectures to effectively handle time series forecasting. This study examines the Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS) model, a novel DL architecture specifically tailored for time series data, using S&P 500 stock price data. The performance of N-BEATS is benchmarked against three baseline models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). The evaluation metrics include Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Results indicate that the N-BEATS model consistently surpasses the other models in all metrics. Additionally, the Diebold-Mariano (DM) test further validates the superior predictive accuracy of the N-BEATS model compared to the alternatives. This research underscores the potential of the N-BEATS model to significantly improve stock price forecasting, offering valuable insights for investors, financial analysts, and other market participants.

Carbon Price Forecasting Based on Influencing Factor Screening and VMD-BIGRU Hybrid Model: A Case of Hubei Carbon Market in China

Carbon Price Forecasting Based on Influencing Factor Screening and VMD-BIGRU Hybrid Model: A Case of Hubei Carbon Market in China

Boosting house price estimations with Multi-Head Gated Attention

Evaluating house prices is crucial for various stakeholders, including homeowners, investors, and policymakers. However, traditional spatial interpolation methods have limitations in capturing the complex spatial relationships that affect property values. To address these challenges, we have developed a new method called Multi-Head Gated Attention for spatial interpolation. Our approach builds upon attention-based interpolation models and incorporates multiple attention heads and gating mechanisms to better capture spatial dependencies and contextual information. Importantly, our model produces embeddings that reduce the dimensionality of the data, enabling simpler models like linear regression to outperform complex ensembling models. We conducted extensive experiments to compare our model with baseline methods and the original attention based interpolation model. The results show a significant improvement in the accuracy of house price predictions, validating the effectiveness of our approach. This research advances the field of spatial interpolation and provides a robust tool for more precise house price evaluation. Our GitHub repository.11Final_file/tree/main/ASI-main. contains the data and code for all datasets, which are available for researchers and practitioners interested in replicating or building upon our work.

Enhancing Stock Price Prediction in the Indonesian Market: a Concave LSTM Approach with RunReLU

This study addresses the pressing need for improved stock price prediction models in the financial markets, focusing on the Indonesian stock market. It introduces an innovative approach that utilizes the custom activation function RunReLU within a Concave Long Short Term Memory (LSTM) framework. The primary objective is to enhance prediction accuracy, ultimately assisting investors and market participants in making more informed decisions. The research methodology used historical stock price data from ten prominent companies listed on the Indonesia Stock Exchange, covering the period from July 6, 2015, to October 14, 2021. Evaluation metrics such as RMSE, MAE, MAPE, and R2 were employed to assess model performance. The results consistently favored the RunReLU-based model over the ReLU-based model, showcasing lower RMSE and MAE values, higher R2 values, and notably reduced MAPE values. These findings underscore the practical applicability of custom activation functions for financial time series data, providing valuable tools for enhancing prediction precision in the dynamic landscape of the Indonesian stock market.

Stock Price Prediction Using Artificial Intelligence and Neural Networks

Predicting the stock prices is a very complex task, and to predict an almost accurate stock price,we need a robust and accurate algorithm which can analyze and compute the longer-term share prices.Several researcher’s equally in the world and different industries have been very interested in the stockmarket. Stock processes are correlated within the nature of the market and that is why it is difficult topredict the share price. This project aims at processing and analyzing huge volumes of data (live data)and running comprehensive algorithms on the dataset. The purpose of the paper is to understand theshortcomings of the current prediction algorithms and to provide a method using neural networks andartificial intelligence through which we can predict the shared values with accuracy.By using the proposed method, anyone can monitor the preferred stock in real-time and can invest in thestock to make the most money by buying a large number of shares at the cheapest price and sellingthem at the highest price..

Hydrogen production from non-potable water resources: A techno-economic investment and operation planning approach

The production of hydrogen through water electrolysis facilitates large-scale energy storage, provides ancillary services, and supports the decarbonization of industries with hard-to-reduce emissions. As renewable energy adoption expands, the demand for these applications is increasing. However, water electrolysis currently faces challenges in cost competitiveness compared to other hydrogen production methods. Additionally, its reliance on potable water sources places additional strain on freshwater reserves. This study addresses these issues by exploring the economically optimal investment and operational strategy for hydrogen production using non-potable water sources. The goal is to maximize net profit through mathematical optimization, utilizing three advanced electrolysis technologies: alkaline electrolysis, proton exchange membrane electrolysis, and solid oxide electrolysis. The study examines three non-potable water sources: seawater, urban wastewater effluent, and rainwater. The model takes into account electricity market values from day-ahead and balancing markets, as well as the market values for hydrogen, oxygen, freshwater, and mixed solid salt. Two scenarios are analysed, reflecting varying levels of uncertainty in electricity price forecasts. Each scenario includes nine case studies, representing various combinations of water sources and electrolysis technologies. The results indicate that alkaline electrolysis and proton exchange membrane electrolysis are financially viable technologies, with potential annual net profits of up to 6.4 and 6.2 million euros, respectively. In contrast, solid oxide electrolysis incurs a negative net profit across all scenarios and is not economically viable under the given conditions. The main revenue sources are hydrogen sales and combined up-balancing and down-balancing services in electricity markets. Participation in the balancing market constitutes a significant portion (35 to 61%) of the total revenue in all cases with positive net profits, making it critical for economic viability. Rainwater is identified as the most cost-effective water source for hydrogen production. However, the costs associated with water treatment and brine management are minimal, contributing only 0.7 to 3.7% to the total cost of hydrogen production. Thus, differences in net profit are primarily attributed to the type of electrolysis technology used.