Mean absolute percentage error

Time Series Models for Forecasting New One-Family Houses Sold in the United States Introduction The economic recession felt in the United States since the collapse of the housing market in 2007 can be seen by various trends in the housing market. This collapse claimed some of the largest financial institutions in the U.S. such as Bear Sterns and Lehman Brothers, as they held over-leveraged positions in the mortgage backed securities market. Credit became widely available to unqualified borrowers

The process of ordering at the Bruce W. Carter Veterans Administration Hospital Affairs (VA) begins with the Purchasing Agent (PA). The PA places orders on the approved vendor website (USfoods.com), based on the needs stated by the Ingredient Control Room (ICR) Worker on the ingredient log. There currently is no forecast tool used, the ICR worker does have par levels that need to be fulfilled. Orders are place on Wednesday to be received on Friday and on Fridays to be received on Monday. The ICR

Tiffany Henault March 3rd, 2015 Quan901-CH2 Forecasting Lost Sales Case Study Section I: Summary Carlson Department store suffered heavy damage from a hurricane on August 31. As a result the store was closed for four months, September through December. Carlson is in dispute with its insurance company regarding the lost sales for the length of time the store was closed. Section II: Problem Identification Two issues to address are the amount of sales Carlson department store would have made if there

National Taiwan University of Science and Technology 10 Artificial Intelligence for Project Management Final Report Instructor: Prof. Rayson Chou Student: Bui Quang Nha : M9705809 Fillip Korsa : E9815001 Table of Contents Abstract 4 1. Introduction 5 1.1. Background 5 1.2. Business needs for pavement life cycle prediction 5 2. Project statement 5 2.1. Problem definition 5 2.2. Project objective 6 3. Data description and preprocessing 8 3.1. Data description 8 3.2. Data preprocessing

Marriott Rooms Forecasting Executive Summary In the case of the Hamilton hotel, Snow needs to make a decision as to if 60 additional rooms reservations should be accepted which could lead to overbooking (Weatherford & Bodily,1990). It is a problem of capacity utilization that is being faced in this particular case where revenue maximization is aimed while minimizing customer dissatisfaction. In this report the case is put forward and various methods have been chosen to come to a sensible conclusion

Introduction New one family house sales from January 1975 through October 2017 has certainly been a roller coaster ride. Unfortunately, the anticipation of climbing that giant hill and the exhilaration of flying down the other side with the occupant’s hands in the air on a roller coaster does not possess the same sentiment when it comes to forecasting new one family house sales. While watching the numbers climb is exciting for economists, the downward decline in sales causes forecasters, economists

classification algorithm that generates one rule for each predictor in the data set. The attributes used for classification are known as predictors and the attribute classified is known as the target. After the generation of rule, the rule with the smallest error is considered as the “one rule.” A frequency table is generated for the creation of rule for a predictor. The results generated with the help of oneR algorithm are easy to interpret and are in easy readable format. Algorithm: For each predictor,

F.6 Physics Laboratory Report Investigation of relationship between pressure, volume and temperature of gas Date of experiment: 12/11/2008 Aim of experiment: The objective of this experiment is: 1. To study the relationship between pressure and volume of a gas at constant temperature. 2. To study the relationship between the volume and temperature of a gas at constant pressure. Principles involved: When gases are compared, their volumes, temperatures and pressure are always involved

determine the percentage error present in the results. As I_OUT MAX=(4π^2 fkN_1 N_2 I)/RL A, the gradient of a graph where peak output current is graphed against peak input current the gradient is equal to (4π^2 fkN_1 N_2)/RL A. Therfore, 0.0715=(4π^2 fkN_1 N_2 I)/RL A 0.0715=(4π^2*50*420*1000*k)/(11.9*0.109)*0.00056 (0.0715*1.9*0.109)/(4π^2*50*420*1000*0.00056)=k

function which closely approximate our data. Trimmed Mean: Involves the calculation of the mean after discarding given parts of a probability distribution or sample at the high and low end, and typically discarding an equal amount of both. This number of points to be discarded is usually given as a percentage of the total number of points, but may also be given as a fixed number of points. Also known as Truncated Mean. For classification trees, phi coefficient, Gini Index, or “twoing”