Price Prediction Research Articles

Forecasting fish prices with an artificial neural network model during the tuna fraud

Agricultural price forecasting plays an important role in stabilising markets and ensuring food security. It provides insights for various stakeholders to optimise planting choices, allocate resources efficiently and mitigate potential risks. However, price forecasting during food safety incidents poses unique challenges. This study focused on a case of tuna fraud in Spain in 2017, which caused 105 people to fall ill and influenced consumer behaviour in fish purchases. To forecast fish prices during an incident of fraud, we used an artificial neural network model (ANN) based on the price of tuna and its substitutes, salmon and hake, as well as a communication index based on the number of posts regarding the tuna fraud from the social media platform X (formerly Twitter). ANN was compared with a threshold vector autoregressive model (TVAR), a classical time series econometric model that offers valuable insights into price dynamics. The results showed that, in the short term, TVAR offers a better price forecast for tuna and salmon, considering the impacts of the X platform. In the medium term, ANN outperformed TVAR. This study contributes to the ANN literature regarding agrifood price forecasting during food safety incidents.

Comparative Analysis of Linear Regression, Decision Tree and Gradient Boosting for Predicting Stock Price of Bank Rakyat Indonesia

An investment is the placement of a current amount of funds in the hope of generating a profit in the future. There are several types of investments, including stocks, which are attractive options as they can bring a huge return to investors. However, rapidly fluctuating stock prices are influenced by various factors, such as company performance, interest rates, economic conditions, and government policies. In Indonesia, PT Bank Rakyat Indonesia Tbk (BBRI) had the largest profit among the 10 largest banks by the end of March 2024, with a profit of IDR 13.8 trillion. The higher the bank's return, the greater the investor's interest in purchasing the stock, influencing the stock price. The goal of stock price prediction is to forecast the stock's future price in order to increase investors' potential profits. Various methods, such as Linear Regression, Decision Tree, and Gradient Boosting, have been developed for stock price prediction. Comparative analysis is needed to determine the most accurate method in predicting the stock price of People's Bank of Indonesia, using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R-squared metrics (R2). The analysis showed that Linear Regression achieved the highest accuracy with R2 of 0.96, MAE of 65.72 and RMSE of 86.74 compared to the Decision Tree and Gradient Boosting models.

ASEAN-5 Stock Indexes Predictions using Geometric Brownian Motion

Even though the ASEAN-5 region has recently experienced enormous economic growth and advancement, this expansion has been associated with increased market volatility. Analysing historical data to assess stock market performance is crucial to comprehending these markets' dynamics and spotting potential hazards and opportunities. This study seeks to determine the mean and volatility parameters of the Geometric Brownian Motion (GBM) model for stock indexes to see trends and patterns in the ASEAN-5 stock market from 2017 to 2022. Once these parameters are determined, they are used in the GBM model to forecast the stock market indexes. Consequently, this research intends to highlight the value of incorporating GBM into stock indexes and assist investors in making short-term price predictions. The geometric Brownian motion involving randomness, volatility, and drift that might aid investors in making sensible investment decisions will be elaborated on in this research.

Integration of Price Data in Wind Power Sales Systems Using Association Rule Mining Combined with Deep Learning Algorithms

With the growing attention of the global population towards renewable energy sources such as wind power derived from natural resources such as Solar, Geothermal, and Wind energy, there is a growing need for genuine estimation of wind energy. Wind energy is important in the supply of electricity in the global energy markets because of its enormous importance in the delivery of renewable energy. As such, it is crucial for accurate appreciation of the power of the wind to ably respond to issues that relate to the trading of power while at the same time addressing issues to do with planning, scheduling and strategic positioning of wind power generation. Therefore, the present work aims to develop a new model known as the Association Rule with DL-based Wind Power Generation and Price Prediction (ARDL-WPGPP). It utilizes two datasets: An Energy dataset includes various columns like tie, wind onshore forecast, and price actual, whereas the weather features dataset includes only wind speed. These datasets are combined to create a dataset where each transaction represents 2 hours of wind generation. A data mining approach is employed to uncover hidden patterns, rules, concepts, and correlations within these datasets, operating on various types of data including quantitative, textual, and multimedia formats. To efficiently extract rules from the dataset, an improved Apriori algorithm is introduced. Subsequently, the generated rules incorporating wind speed are passed into an improved LSTM model, which learns by comparing the label data. The price actual value serves as the label data, assigning labels to data points based on their actual price value. High price actual values indicate high wind power prices, while low values indicate low wind power prices.

A decomposition-ensemble-integration framework for carbon price forecasting

Accurate carbon price forecasting is crucial for effective carbon trading policies that mitigate climate change. The volatility and uncertainty of carbon prices pose significant challenges. Traditional frameworks that use a single model for all intrinsic mode functions (IMFs) fail to leverage the diverse strengths of different models. In this paper, we propose DecEnsInt, a novel framework that utilizes an ensemble of models tailored to distinct frequency domains. By using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose historical prices into IMFs and optimizing ensemble parameters through Sequential Least Squares Programming (SLSQP) for each frequency domain, DecEnsInt enhances forecasting performance. Extensive experiments on four Emissions Trading System (ETS) datasets, evaluated using RMSE, MAPE, MAE and R2, demonstrate DecEnsInt’s superiority, achieving relative improvements in key metrics by 10.58% to 17.72% over runner-up ensemble methods. DecEnsInt also achieves better robustness and stability across different forecasting steps, proving its effectiveness in handling the complexities of carbon price forecasting.

Implementasi CNN-BiLSTM untuk Prediksi Harga Saham Bank Syariah di Indonesia

Stock price forecasting plays a crucial role in stock investment. Accuracy in predicting stock prices can provide significant financial benefits and help reduce investment risks. Stock price data are time series with high-frequency characteristics, non-linearity, and long memory, which makes stock price prediction a complex challenge. This research proposes a method for predicting the stock prices of Islamic banks in Indonesia using CNN-BiLSTM. This method aims to improve prediction accuracy by utilizing the feature extraction capabilities of CNN and the ability of BiLSTM to understand the temporal sequences of stock data. The data used in this research are the closing stock prices of Bank Syariah Indonesia (BSI), Bank Tabungan Pensiunan Negara Syariah (BTPN Syariah), and Bank Panin Dubai Syariah (PDSB) from January 2, 2020, to July 4, 2024. Testing these three stocks yielded MAPE values of 2.376%, 2.092%, and 0.629%, respectively. The study results show that the CNN-BiLSTM prediction model produced has very good accuracy in predicting stock prices.

An ensemble approach for enhanced Day-Ahead price forecasting in electricity markets

Electricity price forecasting (EPF) is a crucial aspect of daily trading operations, enabling market participants to make informed decisions regarding their bidding strategies. This paper explores a day-ahead price forecasting system that harnesses the potential of multiple machine learning (ML) models and their synergistic integration. This approach is designed to capitalize on the strengths of these models while also accounting for the unique characteristics of energy markets. For this purpose, several aggregation models were developed combining the predictions from ML models based on historical evaluations of their performance. The main objective of this approach is to enhance prediction accuracy by shifting the focus away from rigid model selection and instead prioritizing a data-centric approach, by focusing on data quality rather than rigid model selection. As a case study, the German energy market was examined due to its pivotal role within the EU system. The experimental results from this study provide valuable insights into the proposed system’s effectiveness and functionality.

Advanced Machine Learning Techniques for Accurate Forecasting of Crude Palm Oil Price

Advanced Machine Learning Techniques for Accurate Forecasting of Crude Palm Oil Price

Machine learning for real estate valuation: Astana, Kazakhstan case

Purpose of this research is to investigate the accuracy of machine learning models in forecasting and evaluating house prices, and to understand the key factors that impact pricing. The study involved analyzing data scraped from real estate ads in the “sale of secondary housing” category on the website krisha.kz. The paper emphasizes the importance of understanding the factors that affect house prices, such as quality, location, size, and building materials. It was concluded that these factors have a strong correlation with house price prediction. The information available on krisha.kz was found to be a useful resource for finding good apartments. The data collected by the scraper was analyzed by models: Linear regression (LR), interactions linear regression (ILR), robust linear regression (RLR), fine tree regression (FTR), medium tree regression (MTR), coarse tree regression (CTR), linear support vector machine (LSVM), quadratic SVM (QSVM), medium gaussian SVM (MGSVM), rational quadratic gaussian process regression (RQGPR), boosted trees (BoosT), bagged trees (BagT), neural network based on the bayesian regularization algorithm (BR-BPNN). BR-BPNN showed better results than other models, with an MSE of 32.14 and R of 0.9899.

STSB Model Based on STL Decomposition Algorithm and Its Application in Stock Price Prediction Studies

STSB Model Based on STL Decomposition Algorithm and Its Application in Stock Price Prediction Studies

Price Cannibalization Effect on Long-Term Electricity Prices and Profitability of Renewables in the Baltic States

This paper aims to evaluate price cannibalization effects in forecasts of long-term electricity prices and substantiate their relevance on the profitability of renewables in the Baltic States from 2024 to 2033. Statistical data analysis, literature review, scenario method, and PLEXOS modeling were applied. Five scenarios were analyzed for developing renewable energy sources (RES) and load in Lithuania. In contrast, scenarios for Estonia and Latvia were based on assumptions derived from the countries’ national RES strategies. The results showed that the increase in RES capacities will halve electricity market prices from around 130 EUR/MWh in 2024 to 58 EUR/MWh in Latvia, 60 EUR/MWh in Estonia, and 60–77 EUR/MWh in Lithuania in 2033. In time-waving, the absolute and relative price cannibalization effects of renewables were found. In 2033, the loss of revenue from solar photovoltaic (PV) generators was estimated to be 5.5–17.0 EUR/MWh in Lithuania, 7.1 EUR/MWh in Latvia, and 5.6 EUR in Estonia. The case of onshore wind demonstrated revenue losses of 10.5–22.0 EUR/MWh in Lithuania, 12.0 EUR/MWh in Latvia, and 10.0 EUR/MWh in Estonia. After 2029, revenues received by RES electricity generators could not guarantee project profitability; therefore, market flexibility options will be required. The key innovative strategy to mitigate the price cannibalization effect is the demand-side response when leveraging demand flexibility. Typically, this is achieved by sending price signals to the consumers who, if they have any, shift their demand to lower price periods. This is easily applied within HVAC systems, smart electric vehicle charging, and smart home appliance usage. Such behavior would allow the price cannibalization effect to be decreased.

Predicting Stock Prices Using LSTM Networks: A Comprehensive Approach

Abstract: Prediction of stock prices calls for strong algorithmic foundations for predictions of greater magnitude in share prices because the stock market epitomizes volatility. There exist several models used for the prediction of stock prices. The Long ShortTerm Memory algorithm is one model that seems well-suited for such time series problems. The key objective is to best predict current trends in the market and stock prices, which can be done through point prediction, scenario prediction, anomaly prediction, interval prediction, and volatility prediction. The objective of the study is to provide insight to investors and analysts to understand and predict the behavior of the stock market

Stock prediction using Bi-LSTM and RoBERTa sentiment analysis

Stock price forecasting is a widespread research issue in the field of finance. Stock price prediction can give investors more accurate advice and yield higher returns. With the popularity of social media, more and more people choose to express their opinions, and more and more media outlets published news on social media. This paper mainly conducts stock analysis and prediction by presenting the RoBERTa-BiLSTM model with the Bahdanau attention mechanism. It consists of two existing models: the RoBERTa and bidirectional LSTM models. The experimental results show that this model is more accurate in predicting stock prices than the Multi-Layer Perception (MLP) and LSTM models. Inputs to the model include historical AAPL stock price data and sentiment data analysed by news and Twitter tweet sentiment. In this paper, the high-precision RoBERTa-BiLSTM model for stock prediction was proposed, which provides a solution for stock prediction. The practical applications and the typical problem of most studies about stock prediction called the time-shifted problem, are provided in this paper and also have research value.

Sentiment to Stocks: Rule-Based and Deep Learning Sentiment Analysis for LSTM-Driven Stock Prediction

How to accurately predict stock prices is a persistent problem in the financial realm. According to behavioral economics, human actions in the stock market tend to be irrational, emotional, and easily misled. In such context, this papers approach involves adopting various sentiment analysis models in subsequent sections of the paper, which aims to synthesize a precise methodology for forecasting stock prices aligned with the available data, namely structured technical analysis, while considering and quantifying investor sentiment, namely unstructured fundamental analysis. This work points out that by adopting sentiment analysis using the VADER and FinBERT models separately, there are accuracy improvements in both integration of VADER or FinBERT and transactional data compared with merely doing stock price prediction by forecasting using historical data alone. This outcome resemble an valuable proposition of modeling and prediction on irrational human behavior. Nevertheless, this paper provides insights on future possible enhancements in this area, which analyzing additional emotional dimensions in textual data and recognizing the multifaceted nature of human emotions is needed.

GOLD PRICE PREDICTION IN INDONESIA BASED ON INTEREST RATE USING DISTRIBUTED LAG ALMON TRANSFORMATION

Gold is valued for its safety and profitability, driven by steady price changes and influenced by interest rates. Accurately predicting gold prices is very important to make the right investment decisions. This study aims to build a gold price model in Indonesia using the Almon transformation lag distribution and see gold price predictions based on the model that has been built. We used the data on gold prices and interest rates from January 2016 to December 2023. Based on the results of the analysis, the best Almon transformation model used in this study is the Almon model with a maximum lag length of 16 and the second polynomial degree. The prediction results have a MAPE of 16.49%, which shows that the Almon model can predict gold prices well for one year. This study contributes to the understanding of gold price dynamics amid economic variations. However, limitations in the model assumptions should be considered.

Prediction of Resale Value of Pre-Owned Luxury Cars in the Indian Market Employing Machine Learning Techniques

Abstract: The market for second-hand luxury cars in India is witnessing a significant surge, expected to grow at a rate of 16.30% from 2024 to 2032. This growth is fueled by increased car manufacturing, rising disposable incomes, and a shift in consumer preferences towards luxury brands. However, accurately determining the resale value of these vehicles presents a challenge due to various influencing factors. In this dynamic market, informed decision-making is crucial for luxury car buyers. Digital platforms have revolutionized access to real-time market data, helping both buyers and sellers stay updated on pricing trends. Our research explores the complexities of predicting prices for pre-owned luxury cars and introduces a predictive analytics framework using advanced machine learning algorithms. We collected and preprocessed a comprehensive dataset and conducted an in-depth exploratory data analysis. Various regression techniques, including Linear Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting, were employed to forecast prices. These models were evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) to identify the most accurate predictive model. This study offers a systematic solution for price prediction, enhancing the buying process for stakeholders in the second-hand luxury car market

Predicting Edwards Lifesciences Stock Prices Using Machine Learning Model

Predicting stock prices in the stock market is difficult work because the price can fluctuate up and down throughout the trading day, influenced by so many factors. However, the researchers can also use some advanced algorithms to capture the trajectory of the stock market as much as possible. This paper chooses the adjusted closing price as the stock price and aims to predict it for the next trading day based on opening prices, highest prices, lowest prices, volume, and date. Exploratory data analysis is conducted to explore hidden relationships in our stock data. Then, four machine-based learning models: linear regression, K-nearest, support vector machine, and random forest are applied to make predictions for our outcome: adjusted closing price. By comparing the root mean squared error, the model with the best performance is selected and treated as one of the candidates for future stock price prediction. Although these models are relatively fundamental and may not fully capture the complexities of stock prices, they provide a solid foundation for future work.

Application of Attention-Based LSTM Hybrid Models for Stock Price Prediction

The stock market plays a pivotal role in the national economy, while the application of artificial intelligence (AI) in stock price prediction has gained traction. This paper evalu-ates the performance of five advanced deep learning (DL) models: Long Short-Term Memory (LSTM), Self-attention, Convolutional Neural Network-LSTM with attention (CNN-LSTM-attention), Gated Recurrent Unit-LSTM with attention (GRU-LSTM-attention), and CNN-Bidirectional LSTM-GRU with attention (CNN-BiLSTM-GRU-attention), utilizing a decade of data on Amazons closing prices. Our results show that the CNN-BiLSTM-GRU-attention model exhibits superior performance, achieving a root mean square error (RMSE) of 1.054589 and a coefficient of determination (R2) of 0.970123, indicative of its proficiency in handling intricate financial data. This papers significance lies in its validation of the effectiveness of attention-based ensemble models in stock market prediction, as well as the introduction of the innovative application of the CNN-BiLSTM-GRU-attention model in financial forecast-ing, which holds potential for wide-ranging applicability.

Enhancing stock market prediction accuracy with recurrent deep learning models: A case study on the CAC40 index

This paper explores the application of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for stock price prediction over a 10-day horizon. The study aims to compare the predictive performance of these two deep learning architectures within the context of financial forecasting. Utilizing historical stock data from the CAC40 dataset, which represents a capitalization-weighted measure of the 40 most significant stocks on the Euronext Paris, we train and evaluate RNN and LSTM models to forecast future stock prices. Our results demonstrate the superior performance of LSTM networks in capturing the intricate temporal dependencies inherent in stock price data. Compared to standard RNNs, LSTM models exhibit higher accuracy and provide more reliable forecasts over the 10-day prediction period. The specialized memory cells and gating mechanisms in LSTM networks enable them to effectively identify both short-term changes and long-term patterns in stock prices, thus outperforming traditional RNN architectures. This enhanced ability to model the complex dynamics of stock market data underscores the potential of LSTM networks to improve investment decision-making, risk management, and the overall efficiency of financial markets. The insights gained from this study contribute to the growing body of knowledge on the application of deep learning in finance and investment, offering valuable guidance for practitioners and researchers seeking to harness the power of advanced algorithms for stock market prediction and optimization.

Comparative Analysis of LSTM and ARIMA Models in Stock Price Prediction: A Technology Company Example

Stock price forecasting has been an important area of interest for economists and computer scientists. In addition to traditional statistical methods, advanced artificial intelligence techniques such as machine learning can stand out with their ability to process complex data sets and adapt to historical data. In recent years, hybrid models combining deep learning and time series methods have demonstrated superior performance in stock selection and portfolio optimisation. This study comparatively analyses the performance of LSTM and ARIMA models in time series forecasting. In the study, the stock prices of Oracle company are predicted using two different models, LSTM and ARIMA. Model performance is evaluated using metrics like MSE, MAE, RMSE, and MAPE. Both models have been found to be successful in different metrics. The LSTM model has lower error values; meanwhile, the ARIMA model produced proportionally more accurate forecasts. The study concludes that given the potential offered by deep learning, models such as LSTM are essential for time series forecasting. The flexibility of deep learning allows the development of customized models for different data types and time series problems.