Optimizing Predictive Accuracy: A Study of K-Medoids and Backpropagation for MPX2 Oil Sales Forecasting

This study evaluates the use of K-Medoids and Backpropagation methods for predicting MPX2 Oil sales in the automotive workshop industry, which is crucial for meeting customer demands and refining sales strategies. Utilizing transaction data from 2022 to 2023, the study involves normalizing and processing this data with these algorithms to forecast stock levels, focusing on accuracy measures such as Mean Absolute Deviation (MAD) and Mean Squared Error (MSE). K-Medoids assist in identifying customer purchase patterns through clustering, while Backpropagation effectively predicts sales trends, enhancing accuracy through training. Implementing K-Medoids and Backpropagation algorithms in the research resulted in  MSE value of 0.01969 and  MAD value of 0.12200. These values indicate a high level of accuracy in the MPX2 Oil sales predictive model, as lower MSE and MAD values suggest greater accuracy and precision in forecasting. These findings provide valuable insights into the dynamics of MPX2 Oil sales, enabling companies to improve marketing strategies, transaction management, and inventory strategies

Predicting Real Estate Sales Volume in Finland: Building a Predictive Model for the Sales Volume of Old Apartments

The aim of this Master’s Thesis is to find an optimal set of explanatory variables affecting the real estate market in order to build a robust and accurate predictive model that forecasts the development of the real estate sales volume for the next 12 months. In more detail, this research examines the prior literature concerning the factors affecting the real estate market and predictive models based on which the initial variable set is constructed and the model is built. Two interviews are conducted interviewing industry experts in order to gain deeper knowledge of the field. The research aims to answer the following research questions: (1) What factors/input variables to involve when predicting the real estate sales volume, more accurately the sales volume of old apartments, in Finland, (2) What modelling method will give the best result when predicting real estate sales for the next 12 months given the nature of the data and (3) How does the sales volume of old apartments differ based on the apartment’s location and type. Thus, the research tries to build a robust predictive model that can predict the number of old apartments sold in Finland for the next 12 months as accurately as possible. This research is conducted as a both quantitative and qualitative study. In order to connect the results of this study to the existing literature and theoretical framework, five hypotheses were created. The hypotheses in order: (H1), the number of sold old apartments in total will increase within the next 12 months, (H2) the sales volume for old apartments will increase more in the capital region (Helsinki, Espoo and Vantaa) than in other regions, (H3) the sales volume for smaller studio apartments will increase more than for other apartment types, (H4) the economic variables have the biggest impact on the number of house sold and (H5) search query data from Google Trends enhances the model and serves as an important predictor variable. Four models were created to predict the sales volume. Poisson regression and Negative Binomial regression were chosen as the modelling methods given that the response variable represented count data. Based on the results Negative Binomial regression model using predictor variables from Lasso variable selection was the best model as it had the best goodness of fit and thus the best prediction accuracy. Based on the forecasts it seems that the total sales volume of old apartments will increase overall within the next 12 months regardless of the location or type. The growth will be strongest in the capital region followed by Tampere and Turku. Variables related to economy or finance seems to be the most important ones in terms of predicting the sales volume of apartments

Employment in Construction and Distribution Industries: The Impact of the New Jobs Tax Credit

Excerpt] The New Jobs Tax Credit (NJTC) offers a tax credit of fifty percent of the first $4200 of wages per employee for increases in employment of more than two percent over the previous year. Economic theory predicts that such a tax credit should stimulate employment, decrease hours worked per week, and reduce product prices of the subsidized industries. A time series analysis of the construction, retailing, and wholesaling industries finds strong support for these hypotheses. Our results suggest that the NJTC was responsible for 150,000-670,000 of the more than 1-million increase in employment that occurred between mid-1977 and mid-1978 in the construction and retailing industries. Similar analysis indicates that by June 1978, NJTC had produced roughly a 1 percentage point reduction in the margin between retail and wholesale prices of commodities that saved consumers$1.9-$3.6 billion over the course of the previous year

Advertising and Word-of-Mouth Effects on Pre-launch Consumer Interest and Initial Sales of Experience Products

This study examines how consumers' interest in a new experience product develops as a result of advertising and word-of-mouth activities during the pre-launch period. The empirical settings are the U.S. motion picture and video game industries. The focal variables include weekly ad spend, blog volume, online search volume during pre-launch periods, opening-week sales, and product characteristics. We treat pre-launch search volume of keywords as a measure of pre-launch consumer interest in the related product. To identify probable persistent effects among the pre-launch time-series variables, we apply a vector autoregressive modeling approach. We find that blog postings have permanent, trend-setting effects on pre-launch consumer interest in a new product, while advertising has only temporary effects. In the U.S. motion picture industry, the four-week cumulative elasticity of pre-launch consumer interest is 0.187 to advertising and 0.635 to blog postings. In the U.S. video game industry, the elasticities are 0.093 and 1.306, respectively. We also find long-run co-evolution between blog and search volume, which suggests that consumers' interest in the upcoming product cannot grow without bounds for a given level of blog volume