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37 articles for “algorithmic pricing”
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Dynamic Price Model for E-Commerce
Abstract: Retailers may now respond to fluctuating demand, inventory levels, competitor activities, consumer traits, and market circumstances using dynamic pricing, a data-driven technique that has become more significant in e-commerce. Modern pricing systems are much more complex and responsive than they were in the past due to the fast advancements in artificial intelligence (AI), machine learning (ML), big data analytics, and reinforcement learning. This review critically examines recent developments in dynamic …
Published in E-Commerce for Future & Trends · Vol. 13, Issue 2, 2026 · pp. 45–55 Read article
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Stock Market Prediction with LSTM-based Neural Networks: An Empirical Analysis
Abstract: A stock market is a composite of markets and exchanges where the buying and selling of publicly traded company stocks occur on a regular basis. It serves as a marketplace for the stocks of corporations that are available to the public. Companies opt for an initial public offering (IPO) on the primary market as a means to generate capital. People buy stocks primarily in the hope that they may rise …
Published in E-Commerce for Future & Trends · Vol. 10, Issue 1, 2023 · pp. 9–14 Read article
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Gold Price Prediction Using Naïve Bayes Regression Algorithm and ANN
Abstract: AbstractIn this day and age, the amount of stored data has been immensely expanding step by step which is by and large in the organized or unstructured shape and cannot be utilized for any processing to remove valuable data, so a few systems, for example, synopsis, arrangement, grouping, data extraction, expectation and perception are accessible for the same, which goes under the classification of content examination. Forecast is the branch …
Published in Journal of Open Source Developments · Vol. 4, Issue 2, 2017 · pp. 6–9 Read article
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Stock Price Movement Prediction using Machine Learning Algorithms and Time Series Models: A Review
Abstract: The stock market is a very important place for investment in a country. It also serves as the index of the growth for an economy. To determine the behavior of the stock market trends has been the focal point of researchers for very long. The nonlinear structure of the stock market makes it challenging to predict how the stock price will change. But it has been demonstrated that stock market …
Published in Journal of Operating Systems Development & Trends · Vol. 9, Issue 2, 2022 · pp. 7–13 Read article
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Real-Time Cab Fare and ETA Prediction Using API Integration
Abstract: The exponential proliferation of ride-hailing platforms has necessitated the formulation of sophisticated and highly responsive predictive models for cab fare estimation and estimated time of arrival (ETA) computation. This work elucidates a robust framework leveraging real-time application programming interface (API) integration from Uber and Ola within a Flutter-based ecosystem to enhance predictive analytics. By assimilating real-time geospatial data, dynamic pricing algorithms, and latency-optimized API responses, this study investigates the empirical …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 08–15 Read article
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AI-Based House Price Prediction
Abstract: The housing market is one of the most dynamic and significant sectors of any economy, influencing both individual wealth and broader economic stability. Buyers, sellers, investors, and policymakers all rely on accurate housing price predictions. With the advent of artificial intelligence (AI) technologies, particularly machine learning algorithms, the task of house price prediction has seen remarkable advancements. This study provides a detailed overview of AI-based techniques for house price prediction. …
Published in Current Trends in Signal Processing · Vol. 13, Issue 3, 2023 · pp. 1–7 Read article
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Stock Price Prediction Using RNN and LSTM
Abstract: Prediction of stock market has been an attractive topic to the stockbrokers. In stock market the decision on when buying or selling stock is important in order to achieve profit. There are number of techniques that can be used to help investors in order to make a decision for financial gain. In this research work we have proposed a prediction algorithm that will give the relation between the dependent factor …
Published in Journal of Open Source Developments · Vol. 5, Issue 3, 2018 · pp. 26–34 Read article
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Using Elliot Wave Theory and Fibonacci Retracement and in Algorithmic Trading
Abstract: The analysis technique used in predicting stock prices consists of Fundamental analysis and Technical analysis. These analyses are complex in nature and usually unreliable. The algorithmic trading systems offered today consists of primitive technical analysis techniques such as, simple moving averages, exponential moving average, moving averages convergence and diversions and volume weighted average price. The primitive nature of this techniques makes them very unreliable and has a low success rate. …
Published in Trends in Machine design · Vol. 7, Issue 3, 2020 · pp. 22–29 Read article
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Stock Price Prediction Using Data Science Techniques
Abstract: This study centers around persuasively presenting the potential to forecast the stability of future market stocks. Previous research has delved into predicting the trajectory of future market trends, leading to fluctuations in stock data, which opens avenues for refinement. The proposed model employs data science methodologies to predict the stock price index's value. This is achieved by contrasting supervised classification data science learning algorithms that predict either stock price increases …
Published in E-Commerce for Future & Trends · Vol. 10, Issue 2, 2023 · pp. 33–41 Read article
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House Price Estimation Using Linear Regression: A Machine Learning Perspective
Abstract: House price prediction plays a crucial role in the real estate industry, helping buyers, sellers, and investors make well-informed decisions. Accurate estimation of property values enables stakeholders to assess market trends, plan investments, and minimize financial risks. This study focuses on the application of linear regression, a fundamental and widely used machine learning algorithm, to predict house prices based on multiple influencing factors. These factors include location, property size, number …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 · pp. 21–29 Read article
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A Random Forest Approach to Navigating Cryptocurrency Market Fluctuations
Abstract: This study looks at the main elements influencing daily price variations to improve our analysis and prediction of Bitcoin values. Our forecasting algorithm is based on comprehensive data that we have collected and analyzed over the last few years. Because the Random Forest algorithm provides more accurate forecasts than previous techniques, that is why we chose it. Predicting the price swings of cryptocurrencies, like Bitcoin, can be challenging due to …
Published in Journal of Electronic Design Technology · Vol. 15, Issue 2, 2024 · pp. 7–11 Read article
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Optimizing Residential Energy Costs with PEV-based Energy Storage and Particle Swarm Optimization
Abstract: This research addresses the critical challenge of optimizing electrical energy usage amidst the increasing integration of variable renewable energy sources and the widespread adoption of electric vehicles (EVs). The core objective is to alleviate the energy cost burden for residential consumers by leveraging the capacity of Plug-in Electric Vehicles (PEVs) as an auxiliary energy source. Through innovative utilization of PEVs for both charging and discharging actions based on dynamic electricity …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 14, Issue 3, 2024 · pp. 19–29 Read article
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Prediction of Mobile Phone Price Using Machine Learning Classifiers
Abstract: One cannot imagine one's life without mobile phones; in today's digital era, mobile phones have become a necessity for everyone to fulfil their various demands like messaging, communication, entertainment, productivity, research, shopping and many more. In a thriving market of mobile phones where new smartphones are launched every year with new advanced features and various designs, determining the expense of a mobile can be a trouble-some tasks for consumers. In …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 101–108 Read article
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Implementation of K-Means clustering algorithm with technical indicators to identify Profitable stocks
Abstract: In the ever-changing world of stock market trading, accurately predicting price movements is key to maximizing profits. Technical analysis, which looks at past price data to predict future trends, provides valuable insights for investors. This paper delves into using machine learning methods, particularly the K-Means clustering algorithm, along with moving average data, to categorize daily trading patterns. By breaking down the market into clusters and examining the main patterns within …
Published in Current Trends in Signal Processing · Vol. 13, Issue 3, 2023 · pp. 8–14 Read article
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Linear Programming for Profit Optimization in Small-Scale Manufacturing: A Python-Based Simplex and Machine Learning Approach
Abstract: Profit maximization under resource constraints is a classic challenge. Small manufacturers face tight margins and scarce capital every day. This paper tackles that problem using four Python-based methods. The case study is Bintang Bakery in Bandar Lampung, Indonesia. The bakery makes three bread types and faces 18 resource constraints. Data comes from Anggoro et al. Methods tested include LP revised simplex, Differential Evolution, PSO, and ANN Surrogate. General-purpose scipy minimizers …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 01–11 Read article
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Designing an AI-Based Platform for Stock Market Prediction
Abstract: The AI-Based Platform for Stock Market Prediction is an advanced tool designed to forecast stock prices and market trends using artificial intelligence. This platform combines machine learning algorithms, real-time financial data, and sentiment analysis to provide investors with actionable insights. The platform uses advanced predictive techniques like Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines to generate precise and reliable forecasts. Additionally, it incorporates interactive visualizations and portfolio optimization …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 3, 2025 · pp. 14–19 Read article
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Design and Implementation of BLDC Motor Driven Electric Vehicle Using MPPT Controller
Abstract: Minimal and medium power applications benefit greatly from BLDC motor efficiency, high torque-to-inertia ratio, large energy volume, low maintenance requirements and wide speed control range. Resistance found that by deleting phase current sensors and regulating the basic frequency switching of the voltage source inverter (VSI), the proposed control algorithm lowers power losses caused by high frequency switching. With Modern days fuel prices soaring so high for common people, electrical engineers …
Published in Trends in Electrical Engineering · Vol. 12, Issue 1, 2022 · pp. 41–51 Read article
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Exploring the Efficiency of Leading and Lagging Indicators in Algorithmic Trading
Abstract: This paper details a comparison of the overall performance of leading and lagging technical indicators used in algorithmic trading over an extended period. While much of the prior research focuses on index price forecasting and some on statistical arbitrage derived from these predictive techniques, there is a scarcity of studies that assess and evaluate trading strategies. The strategies considered for the study were tested on historical data of the 50 …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 2, 2024 · pp. 8–18 Read article
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Stock Trading Algorithm with Technical Indicators Using Python for Indian Stocks
Abstract: In the financial markets, algorithmic trading has become a potent instrument that enables traders to automate their tactics and profit from market inefficiencies. In this paper, we describe a Python-based algorithmic trading model that makes use of technical indicators and evaluate its performance in comparison to the well-known buy-and-hold approach. By analysing historical price data, these indicators provide insights into the market trends and potential entry or exit points for …
Published in E-Commerce for Future & Trends · Vol. 10, Issue 2, 2023 · pp. 8–17 Read article
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Demand Forecasting for Perishable Food Commodities Using Data Analytics
Abstract: This paper introduces a comprehensive study aimed at enhancing the forecasting of perishable food item demand. Focusing on solving the critical issue of waste management within the supply chain of food products, the research undertakes a comparative analysis of various machine learning models. The development of an optimized model that is capable of accurately forecasting the demand for perishable food items is the focus of this research. The research includes …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 11, Issue 3, 2024 · pp. 27–37 Read article