Combining Advanced Forecasting Methods with System Dynamics - the Case of Finnish Seaports

Seaports play an important part in the wellbeing of a nation. Many nations are highly dependent on foreign trade and most trade is done using sea vessels. This study is part of a larger research project, where a simulation model is required in order to create further analyses on Finnish macro logistical networks. The objective of this study is to create a system dynamic simulation model, which gives an accurate forecast for the development of demand of Finnish seaports up to 2030. The emphasis on this study is to show how it is possible to create a detailed harbor demand System Dynamic model with the help of statistical methods. The used forecasting methods were ARIMA (autoregressive integrated moving average) and regression models. The created simulation model gives a forecast with confidence intervals and allows studying different scenarios. The building process was found to be a useful one and the built model can be expanded to be more detailed. Required capacity for other parts of the Finnish logistical system could easily be included in the model.

Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international market places. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.

The accuracy of present wave forecasting methods : with reference to problems in beach erosion on the New Jersey and Long Island coasts /

Mode of access: Internet.

Events in Indonesia: exploring the limits to formal tourism trends forecasting methods in complex crisis situations

The desire to know the future is as old as humanity. For the tourism industry the demand for accurate foretelling of the future course of events is a task that consumes considerable energy and is of great significance to investors. This paper examines the issue of forecasting by comparing forecasts of inbound tourism made prior to the political and economic crises that engulfed Indonesia from 1997 onwards with actual arrival figures. The paper finds that current methods of forecasting are not able to cope with unexpected crises and other disasters and that alternative methods need to be examined including scenarios, political risk and application of chaos theory. The paper outlines a framework for classifying shocks according to a scale of severity, probability, type of event, level of certainty and suggested forecasting tools for each scale of shock. (C) 2003 Elsevier Science Ltd. All rights reserved.

A study to adapt various sales forecasting methods for use by military club managers

The purpose of this study is to adapt and combine the following methods of sales forecasting: Classical Time-Series Decomposition, Operationally Based Data and Judgmental Forecasting for use by military club managers.

Methods of Telecommunication Technology Forecasting

This master's thesis coversthe concepts of knowledge discovery, data mining and technology forecasting methods in telecommunications. It covers the various aspects of knowledge discoveryin data bases and discusses in detail the methods of data mining and technologyforecasting methods that are used in telecommunications. Main concern in the overall process of this thesis is to emphasize the methods that are being used in technology forecasting for telecommunications and data mining. It tries to answer to some extent to the question of do forecasts create a future? It also describes few difficulties that arise in technology forecasting. This thesis was done as part of my master's studies in Lappeenranta University of Technology.

Wireless e-Business Applications - Methods for Forecasting the Demand of Wireless Services and Technologies

Tämä tutkimus oli osa sähköistä liiketoimintaa ja langattomia sovelluksia tutkivaa projektia ja tutkimuksen tavoitteena oli selvittää ennustamisen rooli päätöksenteko- ja suunnitteluprosessissa ja määrittää parhaiten soveltuvat ja useimmin käytetyt teknologian ennustusmenetelmät. Ennustusmenetelmiä tarkasteltiin erityisesti uuden teknologian ja pitkän aikavälin ennustamisen näkökulmasta. Tutkimus perustui teknologista ennustamista, pitkän aikavälin suunnittelua ja innovaatioprosesseja käsittelevän kirjallisuuden analysointiin. Materiaalin perusteella kuvataan teknologian ennustamista informaation hankkimisvälineenä organisaatioiden suunnitteluprosessin apuna. Työssä arvioidaan myös seuraavat teknologisen ennustamisen menetelmät: trendianalyysi-, Delfoi-, cross-impact analyysi-, morfologinen analyysi- ja skenaario analyysimenetelmä. Työ tuo esille jokaisen ennustusmenetelmä ominaispiirteet, rajoitukset ja sovellusmahdollisuudet. Käyttäen esiteltyjä menetelmiä, saadaan kerättyä hyödyllistä informaatiota tulevaisuuden näkymistä, joita sitten voidaan käyttää hyväksi organisaatioiden suunnitteluprosesseissa.

Demand forecasting process design and methods in medium sized enterprise

The purpose of this thesis was to study the design of demand forecasting processes and management of demand. In literature review were different processes found and forecasting methods and techniques interviewed. Also role of bullwhip effect in supply chain was identified and how to manage it with information sharing operations. In the empirical part of study is at first described current situation and challenges in case company. After that will new way to handle demand introduced with target budget creation and how information sharing with 5 products and a few customers would bring benefits to company. Also the new S&OP process created within this study and organization for it.

Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

Forecasting human entrances at a commercial store using facial recognition data

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting model as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

Forecasting and turning point predictions in a Bayesian panel VAR model

We provide methods for forecasting variables and predicting turning points in panel Bayesian VARs. We specify a flexible model which accounts for both interdependencies in the cross section and time variations in the parameters. Posterior distributions for the parameters are obtained for a particular type of diffuse, for Minnesota-type and for hierarchical priors. Formulas for multistep, multiunit point and average forecasts are provided. An application to the problem of forecasting the growth rate of output and of predicting turning points in the G-7 illustrates the approach. A comparison with alternative forecasting methods is also provided.

Forecasting the effects of global warming on biodiversity

The demand for accurate forecasting of the effects of global warming on biodiversity is growing, but current methods for forecasting have limitations. in this article, we compare and discuss the different uses of four forecasting methods: (1) models that consider species individually, (2) niche-theory models that group species by habitat (more specifically, by environmental conditions under which a species can persist or does persist), (3) general circulation models and coupled ocean-atmosphere-biosphere models, and (4) specics-area curve models that consider all species or large aggregates of species. After outlining the different uses and limitations of these methods, we make eight primary suggestions for improving forecasts. We find that greater use of the fossil record and of modern genetic studies would improve forecasting methods. We note a Quaternary conundrum: While current empirical and theoretical ecological results suggest that many species could be at risk from global warming, during the recent ice ages surprisingly few species became extinct. The potential resolution of this conundrum gives insights into the requirements for more accurate and reliable forecasting. Our eight suggestions also point to constructive synergies in the solution to the different problems.

Preface: Extreme events induced by weather and climate change: evaluation, forecasting and proactive planning

This special issue of Natural Hazards and Earth System Sciences (NHESS) contains eight papers presented as oral or poster contributions in the Natural Hazards NH-1.2 session on"Extreme events induced by weather and climate change: evaluation, forecasting and proactive planning", held at the European Geosciences Union (EGU) General Assembly in Vienna, Austria, on 13-18 April 2008. The aim of the session was to provide an international forum for presenting new results and for discussing innovative ideas and concepts on extreme hydro-meteorological events, including: (i) the assessment of the risk posed by the extreme events, (ii) the expected changes in the frequency and intensity of the events driven by a changing climate and by multiple human- induced causes, (iii) new modelling approaches and original forecasting methods to predict extreme events and their consequences, and (iv) strategies for hazard mitigation and risk reduction, and for a improved adaptation to extreme hydro-meteorological events ...