Ekonomi och prognostisering är en stor utmaning för alla företag och en viktig pusselbit för lönsamheten i byggindustrin. Digitaliseringen har underlättat kontakten mellan entreprenör och projektledning. Med det tryck som digitaliseringen och av andra konkurrerande företag medför är det viktigt att kunna ställa realistiska prognoser med rätta förutsättningar. Detta för att sedan ta vara på utvinna erfarenheter och ha dem i beaktande vid projektering av kommande projekt. Eftersom en väl utförd prognos från varje projekt medför ett underlag till de framtida besluten inom ett företag är det viktigt att hitta och identifiera risker och möjligheter för varje projekt. Bakgrunden för arbetet är behovet att utreda företagets arbetssätt vid prognoshantering. Arbetet utfördes tillsammans med Svevia och mycket av informationen kommer från det företaget. Studien är baserad på intervjuer samt en enkät. De intervjuade personerna hade olika befattningar inom företaget några från företagsledningen och några från produktionen. Brister i dagens arbetssätt synliggjordes och utreddes. Likaså diskuterades frågor kring utbildning, programvaror och resurser. Vid enkäten var respondenterna var anställda på företaget. Resultatet redovisas i form av diagram. Resultaten från enkäten och från intervjuerna analyserades och ledde fram till denna slutsats: brister fanns genom: avsaknad av utbildning, tidsbrist, saknaden av ett enhetligt arbetssätt och kunskapsbrist beträffande programvara. Den viktigaste bristen är saknaden av ett enhetligt arbetssätt, Uppföljning och tillvägagångssätt gällande prognoser borde vara lika för alla anställda. / Economy and forecasting are a big challenge for the corporations and an important piece if the puzzle when it comes to profitability in the construction industry. Digitalization of the workplace within the construction industries has made it easier to communicate between the entrepreneur and project management. A big part of the digitalization and the pressure from other companies makes it very important to be able to make realistic forecasts. Also be able to extract the experience from the forecasts and applying it in the future endeavors of the company. Since a well thought out forecast from every project create a basis in which the future decisions will be grounded in, identifying the risks and possibilities of each project and in how they were resolved to minimize losses will be of much importance. This reports background is the need to investigate a uniform approach to forecast management. This thesis was made in collaboration with Svevia and the bulk of the information was derived from their company. The study is based on interviews conducted with employees and a survey. The respondents held different positions within the company where both members of the management were interviewed and from the production. Shortcomings in current method were made visible and investigated. Likewise issues concerning education, software and resources were discussed. The result of the survey was compiled with diagrams where the employees of the company responded to the different questions. Together with the result of the interviews and the survey the analysis and conclusion was formed. The comprehensive flaws can be attributed to the lack of a uniform practice, where the follow up and approaches are the same for all employees.

<p>The United States Department of Agriculture (USDA) forecasts the Farm Balance Sheet  each year. The Farm Balance Sheet provides an estimate of the value of physical and financial  assets in the United States agriculture sector over time (USDA, 2023). The forecasts evaluated in  this paper are related to assets and debt in the farm sector, including total farm assets, farm assets  real estate, total farm debt, farm debt real estate, and farm debt non-real estate. These forecasts predict the growth in the agricultural sector and help various stakeholders, such as policy makers, USDA program administrators, and agricultural lenders make important decisions. Given the  importance of these forecasts in the agricultural sector, it is the main objective of this research to examine the degree to which the Farm Balance Sheet forecasts are optimal (unbiased and efficient).  During this study, forecasts from the Farm Balance Sheet in the 1986-2021 period are found to be unbiased using Holden and Peel test (1990). Also, using efficiency tests by Nordhaus (1987), it  was found that forecasts from the Farm Balance Sheet are inefficient. This, suggests all the  information is not efficiently incorporated when the forecast is produced .</p>

The gross domestic product (GDP) is an essential measure of the state of economic activity and serves as a crucial tool for policymakers, investors, or businesses. However, the official GDP estimate in the Czech Republic is only available with a lag of approximately 60 days, and the Czech National Bank (CNB) announces its GDP forecast once in each quarter. This thesis focuses on predicting GDP growth in the current quarter, referred to as nowcasting. I employ several methods to nowcast the real GDP growth in the Czech Republic in a pseudo-real-time setting and compare their performance. Additionally, I investigate the possibility of creating an ensemble model by using a weighted average of several nowcasting models. The results suggest that the Dynamic Factor Model (DFM) performs best in the GDP nowcasting task, and its predictive accuracy is comparable with the official CNB nowcast. Furthermore, the model averaging process yields accuracy close to the best individual model while addressing model uncertainty. The GDP nowcast of the DFM will be made available to the public in real-time on a website and updated with a daily frequency.

The Swedish office markets has been emerging the last decade towards a higher rental level equilibrium. The aim of this study is to investigate the fundamental drivers of office rents and modelling of office rent forecasts in five Swedish office submarkets; Stockholm (2), Gothenburg (2) and Malmö (1). The methodology is a combination of economic theory and econometric analysis. The product is an econometric model. By using the estimated drivers, office rent forecasts are modelled and computed based on a vector autoregression-model. Our results show that office stock and vacancy, in lagged fashion, are statistically superior in explaining office rent development. OMX30 was evident to be the largest macro-driver in explaining office rent. The generated forecasts were significant and valid in the CBD-submarkets. However, the forecasts for the Rest of Inner City (RIC)-submarkets were not as precise. The results also show that the forecasts move more linearly compared to the actual office rent data that move more "step-wise".

Courier companies receive delivery orders at different times in advance. Some orders are known long beforehand, some arise with a very short notice. Currently the order delegation, deciding which car is going to drive which order, is performed completely manually by a (TL) where the TL use their experience to guess upcoming orders. If delivery orders could be predicted beforehand, algorithms could create suggestions for vehicle routing and vehicle selection. This thesis used the data set from a Stockholm based courier company. The Stockholm area was divided into zones using agglomerative clustering and K-Means, where the zones were used to group deliveries into time-sliced Origin Destination (OD) matrices. One cell in one OD-matrix contained the number of deliveries from one zone to another during one hour. Long-Short Term Memory (LSTM) Recurrent Neural Networks were used for the prediction. The training features consisted of prior OD-matrices, week day, hour of day, month, precipitation, and the air temperature. The LSTM based approach performed better than the baseline, the Mean Squared Error was reduced from 1.1092 to 0.07705 and the F1 score increased from 41% to 52%. All features except for the precipitation and air temperature contributed noticeably to the prediction power. The result indicates that it is possible to predict some future delivery orders, but that many are random and are independent from prior deliveries. Letting the model train on data as it is observed would likely boost the predictive power. / Budföretag får in leveransorder olika tid i förväg. Vissa order är kända lång tid i förväg, medan andra uppkommer med kort varsel. I dagsläget genomförs orderdelegationen, delegering av vilken bil som kör vilken order, manuellt av en transportledare (TL) där TL använder sin erfarenhet för att gissa framtida order. Om leveransorder skulle kunna förutsägas i förväg kan fordonsrutter och fordonsval föreslås av algoritmer. Denna uppsats använde sig utav ett dataset från ett Stockholmsbaserat budföretag. Stockholmsområdet delades in i zoner med agglomerativ klustring och K-Means, där zoner användes för att gruppera leveranser in i tidsdelade Ursprungsdestinationsmatriser (OD-matriser).  En cell i en OD-matris innehåller antalet leveranser från en zon till en annan under en timme. Neurala nätverk med lång-kortsiktsminne (LSTM) användes för förutsägelsen. Modellen tränades på tidigare OD-matriser, veckodag, timme, månad, nederbörd, och lufttemperatur. Det LSTM-baserade tillvägagångssättet presterade bättre än baslinjen, det genomsnittliga kvadratfelet minskade från 1,1092 till 0,07705 och F1-poängen ökade från 41% till 52%. Nederbörd och lufttemperatur bidrog inte märkbart till förutsägelsens prestation. Resultatet indikerar att det är möjligt att förutse vissa leveransorder, men att en stor andel är slumpmässiga och oberoende från tidigare leveranser. Att låta modellen tränas med nya data när den observeras skulle troligtvis öka prognosförmågan.

On a high-level perspective, fashion is an art defined by fash- ion stylists and designers to express their thoughts and opinions. Lately, fashion have also been defined by digital publishers such as bloggers and online magazines. These digital publishers create fashion by curating and publishing content that is hopefully rel- evant and of high quality for their readers. Within this master’s thesis, fashion forecasting was investigated by applying supervised machine learning techniques. The problem was investigated by training classification learning models on a real world historical fashion dataset. The investigation has shown promising results, where fashion forecasting has been achieved with an average ac- curacy above 65 % . / På en abstrakt nivå definieras mode av stylister och designers.Dessa väljer att uttrycka sina tankar och åsikter genom att skapamode. På senare tid har mode också definierats av digitala förlagsom bloggare och onlinemagasin. Dessa digitala förlag definierarmode genom att skapa och publicera innehåll som förhoppningsvisär relevant och av hög kvalitet för sina läsare. I den här uppsatsen,undersöktes modeprognoser genom att använda sig av övervakademaskininlärningstekniker. Problemet undersöktes genom att läraklassificeringsinlärningsmodeller på ett verkligt historiskt datasetför mode. Undersökningen har visat lovande resultat där modeprognoserhar kunnat nås med en genomsnittlig noggrannhet över 65 %. / Maskininlärning, Förutspå Mode, Mode, Algoritmer, Klassificering

Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing-based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction.

Project schedules should mirror the project, as the project takes place. Accurate project schedules, when updated and revised, reflect the actual progress of construction as performed in the field. Various methods for monitoring progress of construction are successful in their representation of actual construction as it takes place. Progress monitoring techniques clearly identify when we are behind schedule, yet it is less obvious to recognize when we are going to slip behind schedule. This research explores how schedule performance measurement mechanisms are used to recognize construction projects that may potentially slip behind schedule, as well as what type of early warning they provide in order to take corrective action. Such early warning systems help prevent situations where the contractor and/or owner are in denial for a number of months that a possible catastrophe of a project is going to finish on time. This research develops the intellectual framework for schedule control systems, based on a review of control systems in the construction industry. The framework forms the foundation for the development of a schedule control technique for forecasting schedule slippage — the Required Performance Method (RPM). The RPM forecasts the required performance needed for timely project completion, and is based on the contractor's ability to expand future work. The RPM is a paradigm shift from control based on scheduled completion date to control based on required performance. This shift enables forecasts to express concern in terms that are more tangible. Furthermore, the shift represents a focus on what needs to be done to achieve a target completion date, as opposed to the traditional focus on what has been done. The RPM is demonstrated through a case study, revealing its ability to forecast impending schedule slippage. / Master of Science

A tool to model both current rail and future high-speed rail (HSR) corridors has been presented in this work. The model is designed as an addition to the existing TSAM (Transportation System Analysis Model) capabilities of modeling commercial airline and automobile demand. TSAM is a nationwide county to county multimodal demand forecasting tool based on the classical four step process. A variation of the Box-Cox logit model is proposed to best capture the characteristic behavior of rail demand in US. The utility equation uses travel time and travel cost as the decision variables for each model. Additionally, a mode specific geographic constant is applied to the rail mode to model the North-East Corridor (NEC). NEC is of peculiar interest in modeling, as it accounts for most of the rail ridership. The coefficients are computed using Genetic Algorithms. A one county to one station assignment is employed for the station choice model. Modifications are made to the station choice model to replicate choices affected by the ease of access via driving and mass transit. The functions for time and cost inputs for the rail system were developed from the AMTRAK website. These changes and calibration coefficients are incorporated in TSAM. The TSAM model is executed for the present and future years and the predictions are discussed. Sensitivity analysis for cost and speed of the predicted HSR is shown. The model shows the market shift for different modes with the introduction of HSR. Limited data presents the most critical hindrance in improving the model further. The current validation process incorporates essential assumptions and approximations for transfer rates, short trip percentages, and access and egress distances. The challenges for the model posed by limited data are discussed in the model. / Master of Science

Warning systems with the ability to predict floods days in advance can benefit tens of millions of people. Because of these potential impacts there have been efforts to improve prediction systems such as the United States’ Advanced Hydrologic Prediction Service and European-developed Global Flood Awareness System. However, these projects are currently limited to relatively coarse resolutions. This thesis presents a method for downscaling and routing global runoff forecasts generated by the European Centre for Medium-Range Weather Forecasts using the Routing Application for Parallel computatIon of Discharge program that make possible orders of magnitude increases in the density of the resolution of stream forecasts. The processing method involves using the Amazon Web Services to distribute execution in a cloud-computing environment to make it possible to solve for large watersheds with high-density stream networks. Using the Amazon Web Services, the number of streams that can be used in the downscaling process in a twelve-hour period is approximated to be close to five million. In addition, an application for visualizing large high-density stream networks has been created using the Tethys Platform of water resources modeling developed as part of the CI-WATER NSF grant. The web application is tested with the HUC-2 Region 12 watershed network with over 67,000 reaches and is able to display analyzed results to the user for each reach.