This study introduces advanced artificial neural network models designed to address the limitations of traditional statistical methods for water flow forecasting in hydropower reservoirs.

This study’s significance lies in its contribution to the academic discourse on the practical applications of blockchain and smart contracts in a specific regional context. It aims to provide actionable insights for businesses and policymakers, facilitating informed decisions to optimize supply chain processes, bolster competitiveness, and navigate the evolving market context efficiently.

Time series analysis is an essential field in data analysis, particularly within forecasting and prediction domains. Researching and building time series models play a crucial role in understanding and predicting the temporal dynamics of various phenomena. In mathematics, time series data is defined as data points indexed in chronological order and have a consistent time interval between consecutive observations.

This paper concisely overviews recent advancements and challenges associated with applying activity-based models in travel demand forecasting. Additionally, the article explores the operational process of the ActivitySIM model, a specific ABM tool for traffic demand forecasting, by detailing the required input data and parameters.

Accurate forecasting of the electrical load is a critical element for grid operators to make well-informed decisions concerning electricity generation, transmission, and distribution. In this study, an Extreme Learning Machine (ELM) model was proposed and compared with four other machine learning models including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).

Accurate daily load forecasting is critical for effective energy management planning. In this study, the article proposes a new method for daily load forecasting that takes advantage of load data and weather data over time in Tien Giang.

This paper is aimed to apply hybrid machine learning model namely GA-ANFIS, which is a combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA), for the prediction of total bearing capability of driven piles.

The results of this study would be useful in quickly and accurately predicting CPI to the management agencies, investors, construction contractors to pre-plan the construction investment costs. This will also help in suitably adjusting changing construction cost with time.

This article uses Random Forest (RF) and k-fold cross-validation to predict the hourly count of rental bikes (cnt/h) in the city of Seoul (Korea) using information related to rental hour, temperature, humidity, wind speed, visibility, dewpoint, solar radiation, snowfall, and rainfall.

Consumer demand is an important factor in any business, especially in the food retail industry whose products are perishable and have a short life cycle. The daily demand for a food product is affected by external factors, such as seasonality, price reduction and holidays.

Initial Public Offering (IPO) is considered a strategic decision of utmost importance for every business, especially companies that wish to list on the stock exchange. international. Using the Wilcoxon non-parametric test method, the study shows that IPO activities have a positive impact on the profits of businesses listed on the NASDAQ Global Select Market stock exchange in general and of VinFast in particular.

In line with that research trend, this paper proposes a hybrid algorithm combining particle swarm optimization with the simulated annealing technique (PSO-SA) to optimize the length of intervals to improve forecasting accuracies.

This dissertation is composed of two parts, an integrative essay and a set of published papers. The essay and the collection of papers are placed in the context of development and application of time series econometric models in a temporal-axis from 1970s through 2005, with particular focus in the Marketing discipline. The main aim of the integrative essay is on modelling the effects of marketing actions on performance variables, such as sales and market share in competitive markets. Such research required the estimation of two kinds of time series econometric models: multivariate and multiple time series models.

The aim of this thesis is: To examine and evaluate commercial property market forecast theory and models used in forecasting market performance; to define and analyse downside risk exposure and strategies to manage risks in a real estate environment; to test the accuracy of commercial property market and economic forecasts and to determine whether they capture downside risks; to analyse and discuss the relationship between property forecast errors and economic forecast errors; to explore the current state of the australian commercial property market forecasting practice and its exposure to downside risks; to develop a decision making model to improve forecast accuracy of australian commercial property market performance.

Managerial Economics - Homework 4: Estimating and forecasting demand provide students with knowledge about the equation can be rewritten in logarithmic form; the log specification can be transformed into the multiplicative form; sensitivity analysis seeks to determine the sales effect of changes in key explanatory variables;...

General objective: The thesis applies inflation forecasting modelling approach to support monetary policy management in Vietnam, on that basis, proposing policy recommendations to foster the application of inflation forecasting models in operating monetary policy.

The top management needs to follow an organized and systematic process to operate, monitor and control Manpower planning measures in the Organizations. It is obvious that the development and success of an organization are never possible without competent, trained and satisfied people in the organization.

This paper set up a new composite index called economic condition for tourism (ECT) index. This index summarizes the information of the macroeconomic variables which effect the demand for tourism in Thailand.

Forecasting stock index has been received great interest because an accurate prediction of stock index may yield benefits and profits for investors, economists and practitioners. The objective of this study is to develop two efficient forecasting models and compare their performances in one day-ahead forecasting the daily Vietnamese stock index.

The main goal of this study is to investigate the classification capability of several machine learning (ML) techniques, including decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for predicting purchase decisions.