ROC-Based Model Estimation for Forecasting Large Changes in Demand

Forecasting for large changes in demand should benefit from different estimation than that used for estimating mean behavior. We develop a multivariate forecasting model designed for detecting the largest changes across many time series. The model is fit based upon a penalty function that maximizes true positive rates along a relevant false positive rate range and can be used by managers wishing to take action on a small percentage of products likely to change the most in the next time period. We apply the model to a crime dataset and compare results to OLS as the basis for comparisons as well as models that are promising for exceptional demand forecasting such as quantile regression, synthetic data from a Bayesian model, and a power loss model. Using the Partial Area Under the Curve (PAUC) metric, our results show statistical significance, a 35 percent improvement over OLS, and at least a 20 percent improvement over competing methods. We suggest management with an increasing number of products to use our method for forecasting large changes in conjunction with typical magnitude-based methods for forecasting expected demand

Forecasting multivariate volatility in larger dimensions: some practical issues

The importance of covariance modelling has long been recognised in the field of portfolio management and large dimensional multivariate problems are increasingly becoming the focus of research. This paper provides a straightforward and commonsense approach toward investigating whether simpler moving average based correlation forecasting methods have equal predictive accuracy as their more complex multivariate GARCH counterparts for large dimensional problems. We find simpler forecasting techniques do provide equal (and often superior) predictive accuracy in a minimum variance sense. A portfolio allocation problem is used to compare forecasting methods. The global minimum variance portfolio and Model Confidence Set (Hansen, Lunde, and Nason (2003)) are used to compare methods, whilst portfolio weight stability and computational time are also considered.Volatility, multivariate GARCH, portfolio allocation

New forecasting methods for hotel revenue management systems

An accurate forecasting module is a key element of any revenue management system. This module includes demand forecasting, which involves tasks of forecasting complex seasonal time series. The challenge of producing accurate demand forecasts requires the application of suitable forecasting methods to address that complexity. The aim of this paper is to evaluate a new innovation state space modeling framework, based on innovations approach, developed for forecasting time series with complex seasonal patterns. This modeling framework provides an alternative to existing models of exponential smoothing, since it is capable of tackling seasonal complexities such a multiple seasonal periods and high frequency seasonality.info:eu-repo/semantics/publishedVersio

A software service supporting software quality forecasting

Software repositories such as source control, defect tracking systems and project management tools, are used to support the progress of software projects. The exploitation of such data with techniques like forecasting is becoming an increasing need in several domains to support decision-making processes. However, although there exist several statistical tools and languages supporting forecasting, there is a lack of friendly approaches that enable practitioners to exploit the advantages of creating and using such models in their dashboard tools. Therefore, we have developed a modular and flexible forecasting service allowing the interconnection with different kinds of databases/data repositories for creating and exploiting forecasting models based on methods like ARIMA or ETS. The service is open source software, has been developed in Java and R and exposes its functionalities through a REST API. Architecture details are provided, along with functionalities’ description and an example of its use for software quality forecasting.Peer ReviewedPostprint (author's final draft

Local Short Term Electricity Load Forecasting: Automatic Approaches

Short-Term Load Forecasting (STLF) is a fundamental component in the efficient management of power systems, which has been studied intensively over the past 50 years. The emerging development of smart grid technologies is posing new challenges as well as opportunities to STLF. Load data, collected at higher geographical granularity and frequency through thousands of smart meters, allows us to build a more accurate local load forecasting model, which is essential for local optimization of power load through demand side management. With this paper, we show how several existing approaches for STLF are not applicable on local load forecasting, either because of long training time, unstable optimization process, or sensitivity to hyper-parameters. Accordingly, we select five models suitable for local STFL, which can be trained on different time-series with limited intervention from the user. The experiment, which consists of 40 time-series collected at different locations and aggregation levels, revealed that yearly pattern and temperature information are only useful for high aggregation level STLF. On local STLF task, the modified version of double seasonal Holt-Winter proposed in this paper performs relatively well with only 3 months of training data, compared to more complex methods

Volatility forecasting

Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1

Application of a hybrid of least square support vector machine and artificial bee colony for building load forecasting

Accurate load forecasting is an important element for proper planning and management of electricity production. Although load forecasting has been an important area of research, methods for accurate load forecasting is still scarce in the literature. This paper presents a study on a hybrid load forecasting method that combines the Least Square Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) methods for building load forecasting. The performance of the LSSVM-ABC hybrid method was compared to the LSSVM method in building load forecasting problems and the results has shown that the hybrid method is able to substantially improve the load forecasting ability of the LSSVM method

Angler Response to Success in the California Salmon Sportfishery: Evidence and Management Implications

This paper examines effort responsiveness to success in the California salmon partyboat sport fishery. The management process in this important fishery involves setting target harvest levels for both commercial and sportfishing groups and then using closed seasons, restricted gear, and possession limits to dampen effective effort. An important component of the management process involves forecasting sportfishing effort and its effect on catch in order to advance-plan management actions. For want of better information, simple proportionality rules-of-thumb are used currently and this paper examines the plausibility of these. Some simple models forecasting aggregate angler participation and aggregate partyboat catch on a weekly basis are estimated across several different ports. Our findings suggest that anglers are responsive to recent success in several sports (elasticities up to + .5) and that angler participation affects catch with an elasticity exceeding unity. These results indicate that the simple rules of thumb currently in use could be in substantial error.Environmental Economics and Policy, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy,

Theoretical, methodical, and applied aspects of the use of forecasting methods in the process of risk management of hotel and restaurant business

У статті розглянуто теоретико-методичні та прикладні аспекти використання методів прогнозування в процесі управління ризиками готельно-ресторанного бізнесу. На підставі узагальнення та систематизації існуючих методів економічного прогнозування виділено ряд з них, які є найбільш придатними в процесі управління ризиками готельно-ресторанного бізнесу. Вибір конкретного методу прогнозування ризиків готельно-ресторанного бізнесу здійснюється відповідно до мети і завдання розробки прогнозу, періоду упередження, повноти, достовірності та способу представлення інформації про економічні процеси, відносини та явища, обтяжені ризиками. Запропонований набір методів прогнозування ризиків готельно-ресторанного бізнесу дозволить підвищити якість підготовки та прийняття управлінських рішень в процесі управління ними.Hotel and restaurant business is one of the most risky business in the world. The results of the hotel and restaurant business are influenced by: the cyclical nature of consumer demand, the ratio of price and quality of services, weather conditions, environmental and political situation, the degree of development of transport and social infrastructure, location, etc. Reducing the negative impact of various types of risks on the hotel and restaurant business requires search for effective mechanisms for managing them. An important component of such a mechanism is forecasting, which is one of the means of substantiating management decisions in the process of risk management. The presence of a significant number of forecasting methods, of which there are more than 150, makes it difficult to use them in the risk management process of the hotel and restaurant business. Insufficient availability of theoretical and methodological support and practical recommendations on the use of forecasting methods in the process of risk management of hotel and restaurant business determine the urgency of the chosen research direction. The purpose of the article is to improve the theoretical, methodological and applied aspects of using forecasting methods in the process of risk management of hotel and restaurant business. Based on the analysis of existing methods of economic forecasting, a number of methods are identified that are most suitable for use in the process of managing the risks of the hotel and restaurant business. Such methods include: intuitive (or expert) and formalized (or fact graphic) economic and mathematical methods; methods of economic (system) analysis; normative methods; balance method; program-target method. These methods are divided into subgroups and include a certain set of tools. Each proposed method of forecasting the risks of hotel and restaurant business is given a brief description and features of the application. The choice of a specific method for forecasting the risks of hotel and restaurant business is carried out in accordance with the goals and objectives of the development of the forecast, the period of forecasting, completeness, reliability and the way information about economic processes, relationships and phenomena that are burdened with risks. The proposed set of methods for forecasting the risks of hotel and restaurant business will allow not only to anticipate the impact of risks on the financial and economic performance of hotel and restaurant establishments, but also to improve the quality of preparation, adoption and implementation of management decisions in the management process