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All Journal Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Kumar, Aayush
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Modeling the Impact of Holidays and Events on Retail Demand Forecasting in Online Marketing Campaigns using Intervention Analysis Saputra, Jeffri Prayitno Bangkit; Kumar, Aayush
Journal of Digital Market and Digital Currency Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i2.9

Abstract

This study explores the impact of holidays and events on retail demand forecasting using intervention analysis within a SARIMAX model framework. Retail demand forecasting is critical for inventory management and supply chain optimization. Traditional forecasting models often struggle to account for irregular events like holidays, leading to inaccuracies. This study aims to address these limitations by incorporating holidays and events as exogenous variables in the forecasting model. The dataset, consisting of retail sales records across multiple product categories, was preprocessed to handle missing values and standardize date formats. Binary indicators for state holidays and school holidays were created, along with temporal features like the day of the week and hour of the day. The stationarity of the time series was confirmed using the Augmented Dickey-Fuller (ADF) test, with a statistic of -48.67066391486136 and a p-value of 0.0. The SARIMAX model (1, 1, 1)x(1, 1, 1, 24) was developed and evaluated. The model achieved an Akaike Information Criterion (AIC) of 363321.861 and a Bayesian Information Criterion (BIC) of 363375.269. Key coefficients included the state holiday variable at 0 (p-value: 1.000000) and the school holiday variable at 165.2158 (p-value: 0.919689), though neither were statistically significant. Diagnostic checks revealed significant non-normality and heteroscedasticity in the residuals. Forecasting accuracy was assessed using Mean Absolute Error (MAE: 8057.069376036054) and Mean Squared Error (MSE: 809008799.3517022). The Mean Absolute Percentage Error (MAPE) was not computable due to division by zero. Visualizations comparing forecasted versus actual demand highlighted the model’s strengths in capturing general trends and seasonal patterns but indicated challenges in accurately predicting demand during holidays and events. The study underscores the importance of incorporating holidays and events into demand forecasting models and suggests further refinement and the inclusion of additional variables for improved accuracy. Future research should explore alternative modeling approaches and validate findings across multiple datasets to enhance the generalizability and robustness of the forecasting tools.
Decentralizing Identity with Blockchain Technology in Digital Identity Management Kumar, Aayush
Journal of Current Research in Blockchain Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i3.22

Abstract

Blockchain technology has emerged as a promising solution for digital identity verification, offering significant improvements in security, decentralization, and privacy. This study examines the application of blockchain in identity systems, focusing on the benefits and challenges it presents. The findings reveal that blockchain enhances security by 85%, decentralizes data control by 80%, and improves privacy protection by 75% compared to traditional centralized systems. Additionally, the study highlights key challenges, including regulatory uncertainty, scalability issues, and interoperability concerns. Regulatory gaps remain a major obstacle to widespread adoption, despite a rapid increase in blockchain adoption rates from 5% in 2016 to 75% in 2022. Scalability also poses significant technical challenges, with public blockchains struggling to handle large transaction volumes efficiently. Through a comparative analysis, the study shows that blockchain-based identity systems outperform traditional centralized systems in terms of data control (90% vs. 40%), security (85% vs. 50%), and transparency (95% vs. 30%). However, traditional systems still lead in scalability by 10%. This paper concludes that while blockchain holds the potential to revolutionize identity verification, addressing regulatory, scalability, and interoperability issues is critical to achieving its full potential. Future research should focus on developing more scalable consensus mechanisms and standardized frameworks to promote adoption, ensuring blockchain’s viability as a global identity management solution.
Comprehensive Analysis of Twitter Conversations Provides Insights into Dynamic Metaverse Discourse Trends Kumar, Aayush; Hananto, Andhika Rafi
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.2

Abstract

The metaverse, a concept originating from science fiction, has gained substantial traction in recent years as advancements in technology have brought it closer to reality. This virtual shared space, accessed through immersive technologies like virtual reality (VR) and augmented reality (AR), has captivated the imagination of both tech enthusiasts and the general public. This study aims to explore the dynamics of the metaverse discourse by analyzing online discussions across various platforms. We employed a combination of data collection methods, including Twitter API access and web scraping, to gather a diverse dataset of tweets related to the metaverse. Subsequently, the collected data underwent extensive preprocessing to ensure consistency and prepare it for analysis. Our analysis encompassed user statistics, word analysis in tweets, hashtag analysis, and tweet distribution patterns. The findings reveal intriguing insights into user behavior, content trends, and temporal patterns within the metaverse discourse. We observed prominent usernames, geographic distributions of users, prevalent words and hashtags, as well as temporal fluctuations in tweet activity. For instance, the most common username is "Fatemeh ashoobian" with 800 users, indicating a significant presence in the metaverse community. Furthermore, the number of tweets about the metaverse per day over a certain period shows daily fluctuations with the highest peak on November 14, 2023. These insights contribute to a deeper understanding of the metaverse ecosystem and its implications for society, technology, and culture. Through our research, we aim to provide valuable insights to stakeholders across various sectors, including technology developers, policymakers, content creators, and end-users. By understanding the emergent trends and themes within the metaverse discourse, stakeholders can navigate this rapidly evolving landscape more effectively and harness its transformative potential for the benefit of humanity.
Co-Authors Bangkit, Jeffri Prayitno Hananto, Andhika Rafi