In recent years, the importance of supply chain management has been attracting attention. An increasing number of companies are adopting Vendor Managed Inventory (VMI), one of the effective supply chain management methods. VMI is built on the cloud and has problems such as a lack of data transparency and traceability and a single point of failure due to the cloud. In addition, to efficiently deliver VMI, vendors need experience and know-how in demand forecasting, and inexperienced vendors cannot forecast demand adequately. This study proposes a new configuration of blockchain-based VMI using smart contracts that perform statistical processing to aid in demand forecasting. Including a demand forecasting smart contract in the VMI configuration can add accuracy and reliability to the demand forecasting results of inexperienced VMI managers. In this study, we employ ABC analysis as one of the statistical processes that help in demand forecasting and show its feasibility by implementing and evaluating it using a smart contract.
Summary This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N‐BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.
Karar bilimi karar verme işini kolaylaştırmak ve geliştirmek için eldeki sınırlı bilgiyi kullanarak pek çok teknikten faydalanır. Bu nedenle ekonomi, istatistik, üretim yönetimi ve kontrolü ve psikoloji gibi bilim dallarını da içeren disiplinler arası bir alandır. Sürekli olarak karşı karşıya kalınan karar verme durumu neticesinde verilen kararlar ve sonrasında atılan adımlar ise geleceği şekillendirmektedir. Bu nedenle karar biliminin günümüzdeki yeri oldukça önemlidir. Bu çalışmada 2012-2021 yılları içerisinde karar bilimi alanında üretilen bilimsel çıktıların değerlendirilmesi amaçlanmaktadır. Bu amaçla Scopus/SciVal veri tabanı üzerinden ulaşılan 508.220 bilimsel çıktı incelenmiş, yıllara göre bilimsel çıktı sayısı, atıf sayısı, görüntülenme sayısı bilgileri paylaşılmıştır. Dünya genelinde üretilen bilimsel çıktıları kapsayan bu çalışmada karar bilimi alanında en fazla bilimsel çıktının 2021 yılında (95.109) üretildiği ve en fazla bilimsel çıktıya sahip ülkenin Çin (106.752) olduğu sonucuna ulaşılmıştır. Ayrıca en fazla bilimsel çıktıya sahip enstitü/üniversitenin CNRS (10.411) ve en fazla bilimsel çıktıya yer veren derginin “IFIP Advances in Information and Communication Technology” (10.084) olduğu belirlenmiştir. Bilimsel çıktı sayısı dikkate alındığında yapılan çalışmalarda daha çok kurumsal işbirliklerinin tercih edildiği (201.933) ve karar bilimi alanı içerisinde en fazla çalışılan konuların “Bitcoin; Ethereum; Nesnelerin İnterneti” (16473) olduğu sonucuna ulaşılmıştır. Genel olarak yapılan bu çalışma karar bilimi alanında çalışan araştırmacılar için bilgilendirme, değerlendirme ve yönlendirme özelliklerini taşımaktadır.
Richard De Freitas Pinto, Viviane Fagundes de [UNESP] Mattos, Luiz Ricardo Nakamura
Esse trabalho apresenta a modelagem do volume mensal de transações da criptmoeda Ethereum por meio da metodologia de Box-Jenkins, envolvendo as etapas: análise exploratória, identificação, estimação e validação, algumas das quais executadas com a utilização de diferentes técnicas. O modelo encontrado pela modelagem SARIMA (Modelo autoregressivo integrado de médias móveis sazonal) conseguiu descrever o comportamento linear dos dados de forma satisfatória, mas não foi suficiente para descrever o comportamento da série, composta por movimento linear e não linear, sendo melhor representada por um modelo híbrido.
The rapid expansion of the non-fungible token (NFT) market has attracted many investors. However, studies on the NFT price fluctuations have been relatively limited. To date, the machine learning approach has not been used to demonstrate a specific error in NFT sale price fluctuation prediction. The aim of this study was to develop a prediction model for NFT price fluctuations using the NFT trading information obtained from OpenSea, the world’s largest NFT marketplace. We used Python programs to collect data and summarized them as: NFT information, collection information, and related account information. AdaBoost and Random Forest (RF) algorithms were employed to predict the sale price and price fluctuation of NFTs using regression and classification models, respectively. We found that the NFT related account information, especially the number of favorites and activity status of creators, confer a good predictive power to both the models. AdaBoost in the regression model had more accurate predictions, the root mean square error (RMSE) in predicting NFT sale price was 0.047. In predicting NFT sale price fluctuations, RF performed better, which the area under the curve (AUC) reached 0.956. We suggest that investors should pay more attention to the information of NFT creators. We anticipate that these prediction models will reduce the number of investment failures for the investors.
The purpose of this research is to compare ARIMA and Prophet algorithms and find the best algorithm for forecasting bitcoin prices. The dataset is two years historical bitcoin data between February 2019 and 2021. The data is segmented into daily, weekly, and monthly period category. Both algorithms are built into a univariate model that only receive 2 features for training the model. Several ARIMA models is developed for each dataset interval. After that, the parameter of each model will be cross-referenced to each other to obtain the best parameter combination. Meanwhile, Prophet model will be developed using automatic and manual tuning. Then again parameter value of each model will be cross-referenced to each other to obtain the best parameter combination. Evaluation of the training model is done by calculating the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Rooted Mean Squared Error (RMSE). The results showed that the best model for the daily and weekly data category was the Prophet algorithm, while for the monthly data category was the ARIMA algorithm.
In this paper, we investigate how to forecast Non-Fungible Token (NFT) sale prices by using multiple multivariate time series datasets containing features related to the NFT market space. We examined eight recent studies regarding the forecasting and valuation of NFTs and compared their most important findings. This laid the fundamental work for two separate machine learning prototypes based on Long Short-Term Memory (LSTM) which are able to forecast the sale price history of an individual NFT asset. Root Mean Squared Errors (RMSE) of 0.2975 and 0.24 were obtained which appears to be promising.
This study uses the API of Upbit, one of Korea’s cryptocurrency exchanges, to predict continuous time series for a limited period and cryptocurrencies using LSTM, a machine learning technique. The trading (buying and selling) point algorithm presented in this study was used to conduct experimental research on efficient profit creation for cryptocurrency investment. Several related studies have shown the results of time series prediction for long-term forecasts, such as a week or several months. Still, they have not attempted to make an ultra-short-term prediction in units of one minute. This paper attempts such a 1 min prediction. This is an experiment to create efficient profits by setting efficient trading (buying and selling) points using machine learning techniques and repeating these operations by an algorithm. Applying it to cryptocurrency shows the possibility of time series prediction.
This study analysis forecasting the bitcoin exchange rate against the USD. The dataset selected for this study starts from January 2015 to June 2022. This study's methodology uses autoregressive integrated moving average forecasting (ARIMA). The overall outcomes of this study were gathered from the statistical software Minitab 21.1. The Box Jenkins approaches are also used to predict the best model. To determine the ARIMA model parameter, this study did autocorrelation function (ACF) and partial autocorrelation function (PACF) analyses. According to the Box-Cox transformation method, log transformation was selected. The outcome demonstrates that the seasonal with the regular difference in the Bitcoin exchange rate against the USD is a stationary data series. The forecasting model used in this study is ARIMA (1,1,0) (2,1,1)12. This predicted model is identified through the Mean squared error by comparing the other guessing ARIMA models. After the prediction, 5 Month bitcoin exchange rate against the USD. Investors will be able to estimate the bitcoin exchange rate against the USD with the use of this information, but volatility must also be properly watched. This will aid investors in making better investment decisions and increase profits. In future studies, better consider another exchange rate of BTC and software experts will develop such type of software based on ARIMA models for prediction.
Cryptocurrencies (CTC) are decentralised digital currency. In the past decade, there has been a massive increase in its usage due to the advancement made in the field of blockchain. Bitcoin (BTC) is the first decentralised CTC which garnered a lot of attention from the media as well as the public due to its ability to sustain the momentum in the market. However, investing in BTC is not the first choice of the investor due to the market’s erratic behaviour, price volatility and lack of a model that could be used to predict its price. In this direction, the present study aims in developing a time-series forecasting model that can efficiently as well as effectively predict the price of Bitcoins. For this purpose three machine learning (ML) models namely Long Short Term Memory (LSTM), Autoregressive Integrated Moving Average method (ARIMA) and Seasonal Autoregressive Integrated Moving Average method (SARIMA) models have been employed which are statistically scrutinised on the basis of the performance metrics namely Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The computed value of RMSE, MAPE and $\mathrm{R}^{2}$ for the LSTM model is 1447.648, 3.059% and 0.9702 respectively, ARIMA model is 1288.5, 3.479% and 0.9566 respectively and the SARIMA model is 1802.31, 4.665% and 0.9505 respectively.
Dimitri Mahayana, Shafa Amarsya Madyaratri, Muhammad Fadhl 'Abbas
This research proposes a machine learning-based system that aims to form an investment strategy that is capable of buying and selling cryptocurrencies. Predicting the price movement of crypto assets has a number of challenges, especially due to the high volatility of trading prices. Traditionally this process is carried out by conducting technical analysis using technical indicators which requires extensive knowledge and experience to get maximum profit in cryptocurrency trading. The CRISP-DM methodology was applied to create a classification model to predict the movement of the BTCUSDT cryptocurrency pair using a tree-based classification algorithm with the Gradient Boosting framework, namely the Light Gradient Boosting Machine (LGBM). The Logistic Regression algorithm was used as a comparison. As input data, the klines dataset of the BTCUSDT cryptocurrency pair is used and feature engineering is carried out in the form of labelling (up/down) as well as a number of technical indicators commonly used in trading activities. The prediction results of the classification model are then used in the further evaluation stage by being tested (used for trading) against the original data of BTCUSDT price movements for one month and the Return on Investment (ROI) obtained is evaluated against the Buy and Hold strategy. Based on the evaluation results, the resulting model has a better performance than Logistic Regression, but the model's performance has not been able to exceed the ROI value of the Buy and Hold strategy consistently and still can't generate profit.
How to predict the change trend of asset prices in the future and decide different operation modes in advance to obtain the maximum benefits is the concern of investors. Taking gold and bitcoin as examples, this paper develops an appropriate mathematical model that uses only the past daily price stream to help traders determine whether to buy, hold or sell assets in their portfolio every day. At the same time, the robustness of the model is analyzed by robustness. The study found that holding US $1000 on September 11, 2016 will eventually maximize profits on September 10, 2021.
To be or not to be is the question that Hamlet thinks about day and night. Gold or Bitcoins is an inescapable choice for investors. With the ever rising and falling price of gold and bitcoin, making good trading decisions is of paramount importance. In this paper, we systematically investigate how data can be used to quantify the factors that influence trading and make the final decision. We build time series with the prices of gold and bitcoin for the past five years. We obtained forecast curves with excellent fit by seasonality analysis and ARIMA time series model forecasts.
In the actual trading process, investors can only give the best daily trading strategy based on the past price data of gold and bitcoin, then they need to predict and evaluate the trend of the investment items in the coming period and plan out the trading scheme in advance. We also draw on data from many investment questionnaires on websites such as Stock Market Analysis & Tools for Investors to give specific trading strategies. We choose the XGBoost regression price prediction model and enable the genetic algorithm to find the best learning rate parameters. The first 100 trading days of gold and bitcoin data are taken separately for learning training tests, and then the first 20 data are used to predict the price trend for the next five days, which is repeated every day. It provides more accurate prediction results based on the latest prices. An optimization model is established to increase the final investment value by judging the buying and selling indexes by whether the expected return exceeds the purchased commission.
Portfolio optimization is one of the most complex problems in the financial field, and technical analysis is a popular tool to find an optimal solution that maximizes the yields. This paper establishes a portfolio optimization model consisting of a weighted unidirectional dual-layer LSTM model and an SMA-slope strategy. The weighted unidirectional dual-layer LSTM model is developed to predict the daily prices of gold/Bitcoin, which addresses the traditional problem of prediction lag. Based on the predicted prices and comparison of two representative investment strategies, simple moving average (SMA) and Bollinger bands (BB), this paper adopts a new investment strategy, SMA-slope strategy, which introduces the concept of k-slope to measure the daily ups and downs of gold/Bitcoin. As two typical financial products, gold and Bitcoin are opposite in terms of their characteristics, which may represent many existing financial products in investors’ portfolios. With a principle of $1000, this paper conducts a five-year simulation of gold and Bitcoin trading from 11 September 2016 to 10 September 2021. To compensate for the SMA and BB that may miss buying and selling points, 4 different parameters’ values in the k-slope are obtained through particle swarm optimization simulation. Also, the simulation results imply that the proposed portfolio optimization model contributes to helping investors make investment decisions with high profitability.
this study aims to analyze the impact of data selection to train machine learning models and forecast Bitcoin prices. Specifically, we train elastic net regularization models using two datasets with almost identical total observations. One dataset emphasizes years of observations (depth) over total variables, while the second one emphasizes the number of variables (width) over years of data. Our results suggest that the dataset with more extended historical time series and fewer variables provides a lower forecasting error than the dataset with shorter time series and more variables. Our results may be helpful to practitioners looking to identify data selection strategies to train ML-based forecasting models.
K Dhinakaran, Baby Shamini P, J Divya, C Indhumathi · 5 authors
One of the most valuable currency across the globe right now is Cryptocurrency. Apart from being highly valued, its value increased from approximately 1 dollar in 2010 to 57521,576 in 2021 (for Bitcoin). Again, in recent years, it has attracted considerable attention in a variety of fields, including economics and computer science. The former focuses on studies to determine price fluctuations and its future prices for factors that determine how it will affect the market. The latter mainly focuses on its vulnerabilities, scalability and other techno-cryptocurrency issues. Its aim is to reveal the advantage of the traditional Autoregressive Integrative Moving Average (ARIMA) model in estimating the future value of cryptocurrency by analysing the price time series over a period of 3 years. On one hand, the factual studies show that the conduct of the time series is nearly unchanged, this simple scheme is efficient in sub-periods for the most part when it is used for short-term prediction, the further investigation in Cryptocurrency prediction of the price using an ARIMA model which has been trained over the whole dataset, as well as a limited part of the history of the Cryptocurrency price, with the input of length being w. The interaction of the prediction accuracy and choice of window size is well highlighted in the work.
In this paper, we aimed to build a quantitative investment trading model based on a combination of a multivariate cycle ARMA model and Apriori. We first note that in order to have a sound investment strategy, a forecast for the next trading day needs to be made. To do this, a basic time series forecasting model was first built to predict the value of gold and bitcoin for the next day based on the market volatility of the previous 40 days. The next step is developing a trading strategy model with a stable rate of return and some risk tolerance. At the same time, we developed a fixed stop-loss strategy to protect the strategy's stability and improve the risk resistance performance. Ultimately, using this model, we calculated that on 10 September 2021, we will have a return of $4816941 in Bitcoin and $1129.0503 in gold.
The COVID-19 pandemic has led to a decentralization of the workforce in many industries. Due to the stay-at-home orders to control the spread of the virus, many are working from home. Even though modern technological advancements have helped some companies adapt to this new norm, many others are still scrambling to find the best way to remotely manage employees and accommodate their needs. Our research shows that the current challenges organizations face in managing their human capital are like the ones they face due to workplace demographic changes. This study focuses on analyzing those challenges and how human competency can be unlocked and developed to encourage sustainable autonomous working in an office, at home, or during frequent traveling. This study investigates the challenges faced by both organizations and employees, and presents a new business model that helps with the sustainable use of human resources and improves employee efficiency.
Yash Wadalkar, Yellamraju V H Sai Tarun, Jaiesh Singhal, Reena Sonkusare
Bitcoin, one of the most famous and high-in- demand cryptocurrencies, is a type of digital asset that is extremely difficult to track and make predictions upon. In addition, Bitcoin price does not correlate with market- movements, therefore, predicting its price action and its locus is an ordeal. In this paper, we have followed a comparative analysis approach, wherein we are using four different models to predict the trend of BTC Time series data. The results justify that the models have achieved accurate forecasting trends. During the period of 16th to 31st December 2020, Bitcoin prices experienced considerably high swings, due to the increased demand for it. In quantitative terms, the prices experienced fluctuations to the tune of 8000 USD. Despite these enormous price changes, we were able to achieve a model, that helped us attain a Mean Absolute Error (MAE) of 153.55 USD and Mean Square Error (MSE) of 43231.80 USD. Conventional Bitcoin price predicting researches follow a single to two model approach. However, for a highly volatile asset like Bitcoin, making long-term predictions and generalizing them based on limited number of models results in low accuracy outputs. This gap has been bridged in our research, we have worked with different models, as well as fragmented the time intervals into smaller portions, post which the prediction was made for only 2 days. Using this approach, we attained results with least error rates. The results obtained clearly show that ARIMA is the best model for predicting the future trends for BTC time series data. It takes into account the different types of decompositions like Regular Trend, Sessional and Residual Trend making the model give the best results.