ABSTRACT Recent empirical results show that forecast combinations and cross-learning schemes are winning approaches in the time series field. Although many competition-winning combination methods - with cross-learning or not - use static weights along the forecasting horizon, we could not find extensive work about the effects of using horizon-optimized weights. This paper proposes a forecast combination framework and provides a considerably sizeable empirical investigation into the use of horizon-optimized weights, i.e., weights that may vary over the forecasting horizon. We build on cross-learning, time series clustering and cross-validation to form Horizon-Optimized Convex Combinations (HOC2) of forecasts from five methods: Automated exponential smoothing, Automated ARIMA, Theta, TBATS, and Seasonal naïve. Our combinations were tested with data from the previous M1, M3 and M4 forecast competitions, comprising 104,004 time series with different frequencies and lengths. The results shall be helpful to support future research on how horizon-optimized weights can be used interchangeably with static ones.

RESUMO Os resultados empíricos na literatura demonstram que a versão condicional do Modelo de Precificação de Ativos Financeiros (CAPM), particularmente no que se refere ao modelo na forma em espaço de estado, no qual o beta é estimado pelo filtro de Kalman, possui maior poder explicativo do que a sua versão incondicional. A maioria das análises empíricas na literatura baseia-se em portfólios de ações para explicar anomalias financeiras, porém poucos estudos propõem-se a melhorar a avaliação de desempenho de fundos de investimento. A principal contribuição deste artigo consiste em avaliar fundos de investimento brasileiros por meio de medidas tradicionais estimadas a partir do CAPM na forma em espaço de estado com erros heteroscedásticos e homoscedásticos e comparar seus resultados com modelos alternativos, tais como o CAPM incondicional e o modelo de quatro fatores. Utilizando uma amostra de fundos de ações, os resultados indicam que o modelo CAPM condicional produz melhores resultados do que os modelos alternativos, proporcionando melhores práticas de avaliação de desempenho em relação às habilidades de stock-picking e market-timing.

ABSTRACT Empirical studies have revealed that the conditional Capital Asset Pricing Model (CAPM) has a higher explanatory power than its unconditional version, particularly for the model in state-space form where the beta is estimated using Kalman filter. Most empirical analyses are based on stock portfolios to explain financial anomalies, but only a few studies proposed improving investment fund performance. The main contribution of this study is the assessment of Brazilian investment funds through traditional measures estimated from the CAPM model in state-space form with heteroscedastic and homoscedastic errors compared to alternative models, such as the unconditional CAPM and a four-factor model. Using a sample of stock funds from May 2005-April 2015, the results indicate that the conditional CAPM model produces better results than the alternative models, providing better performance evaluation practices for funds in both stock-picking and market-timing ability.

RESUMEN Los resultados empíricos en la literatura revelan que la versión condicional del CAPM, particularmente con respecto al modelo en forma de espacio de estado, en el cual se estima beta mediante el filtro de Kalman, posee mayor poder explicativo que su versión incondicional. La mayoría de los análisis empíricos se basan en carteras de valores para explicar anomalías financieras, pero pocos estudios proponen mejorar el rendimiento de los fondos de inversión. La principal contribución de este estudio a la literatura es que lleva a cabo la evaluación de fondos de inversión a través de medidas condicionales generadas a partir del CAPM en forma espacio-estado con errores heteroscedásticos y homoscedásticos y que compara sus resultados con modelos alternativos, tales como CAPM incondicional, modelo de cuatro factores. Utilizando una muestra de fondos de acciones, los resultados indican que el modelo CAPM condicional produce mejores resultados que los modelos alternativos, proporcionando mejores prácticas de evaluación de desempeño en relación con las habilidades de stock-picking y market-timing.

ABSTRACT This paper describes a study of the dispatch planning/scheduling process for inbound containers handled with a reach stacker. Client container pickup is scheduled at least one day in advance for one of six two-hour time windows (six five-container-high stacks per time window) on a given day. A buffer area is available for the containers to be moved in when clients are being served. The aim of this study was to determine the conditions required to ensure that all the containers are dispatched within the scheduled time window and so meet the clients’ requirements. To this end, the performance indicators were identified and compared using simulations as an analytical tool. The results indicate that the shortest-processing-time (SPT) queueing discipline is preferable to the first-come-first-served (FCFS) discipline and that client arrivals can usefully be restricted to periods shorter than two hours in order to meet container-dispatch and service-quality objectives.

Resumen Una parte fundamental del proceso de previsión probabilística de energía eólica es tener en cuenta las previsiones de la velocidad del viento. Para obtener pronósticos probabilísticos precisos de la producción eólica ha sido desarrollada una metodología híbrida utilizando técnicas no paramétricas conocidas como SSA (Análisis Singular Espectral) y Estimación Condicional de la Densidad por Kernel (CKDE). SSA es empleada para predecir la velocidad del viento y CKDE para obtener previsiones probabilísticas de la energía eólica, dado que la generación de la energía eólica tiene una relación no lineal con la velocidad del viento y ambas son variables aleatorias distribuidas que siguen una función de densidad conjunta. Haciendo uso de una base de datos horaria que incluye la velocidad del viento y la energía eólica es ilustrada dicha metodología. Una vez que las previsiones de la velocidad del viento son obtenidas, los correspondientes pronósticos probabilísticos de la generación de energía eólica son estimados para un horizonte de 24 horas. Los resultados obtenidos son comparados con otras metodologías existentes.

Abstract A fundamental part of the probabilistic forecasting of wind energy process is to take into account wind speed forecasts. To achieve accurate probabilistic forecast of wind output, we developed a hybrid methodology using a nonparametric technique known as SSA (Singular Spectrum Analysis) and (CKDE) Conditional Kernel Density Estimation. SSA is employed to forecast wind speed and CKDE to obtain probabilistic forecasts of wind energy, based on the fact that wind power generation has a nonlinear relation with the wind speed and both are random variables distributed according to a joint density function. An hourly wind dataset including wind speed and wind power is used to illustrate the approach. Once the wind speed forecasts are obtained the corresponding probabilistic forecast of the wind power generation is estimated for a lead time of 24 hours ahead. The results obtained are compared with other existing methodologies.

ABSTRACT The Brazilian electricity energy matrix is essentially formed by hydraulic sources which currently account for 70% of the installed capacity. One of the most important characteristics of a generation system with hydro predominance is the strong dependence on the inflow regimes. Nowadays, the Brazilian power sector uses the PAR(p) model to generate scenarios for hydrological inflows. This approach does not consider any exogenous information that may affect hydrological regimes. The main objective of this paper is to infer on the influence of climatic events in water inflows as a way to improve the model’s performance. The proposed model is called “causal PAR(p)” and considers exogenous variables, such as El Niño and Sunspots, to generate scenarios for some Brazilian reservoirs. The result shows that the error measures decrease approximately 3%. This improvement indicates that the inclusion of climate variables to model and simulate the inflows time series is a valid exercise and should be taken into consideration.

Resumo: No Brasil, o comportamento da carga, em especial do consumo de energia, tem sido amplamente investigado nos últimos anos. Esse interesse, em geral, é devido à grande importância financeira e social desse insumo, pois sua falta pode causar todo tipo de dano ao país. O objetivo do presente trabalho é a geração de uma série mensal de carga elétrica livre das variações de ofensores não econômicos, no caso, calendário e temperatura. Foram comparadas duas abordagens com vistas à seleção da mais eficiente na remoção dos efeitos dos referidos ofensores: a primeira de natureza empírica e a segunda com características híbridas, utilizando métodos empíricos e modelos de Séries Temporais. Os dados utilizados são provenientes de observações diárias de cada um dos quatro subsistemas que integram o Sistema Interligado Nacional (SIN), porém a ideia é produzir séries mensais do SIN e não apenas de cada um dos subsistemas. A série trimestral do PIB foi utilizada para decidir qual abordagem melhor ajustou os dados de carga. Verificou-se que a diferença dos ajustes é mínima entre os métodos propostos, apresentando alto poder de explicação quando comparadas à série sem a retirada dos ofensores calendário e temperatura.

Abstract: In Brazil, the behavior of electrical load, particularly in energy consumption, has been widely investigated over the past years. In general, this interest is due to the great financial and social importance of this input, as its failure or shortage can have a variety of damaging impacts to the country. This paper proposes a method to generate monthly load series freed from variations arising from two sources: calendar and temperature. To find the best fitting approach to removing these effects, we considered a totally empirical method and one with hybrid features, as it uses both empirical procedures and time series models. The data set used comes from daily observations from each one of the four subsystems that form the Brazilian Electricity Grid. However, the final task is to obtain unique monthly series for the entire grid, and not only the four subsystems. The quarterly GDP series was used to check the performance of the two proposed methods. It was noted that the adjustment difference is minimal in the two approaches studied, and that both series had a great explanatory power when compared with the time series without removing calendar and temperature effects.

The periodic autoregressive model, a particular structure of the Box & Jenkins family, denoted by PAR (p), is employed to model the series of hydrological streamflow used for estimating the operational costs of the Brazilian hydro-thermal optimal dispatch. Recently, some aspects of this approachbegan to be studied and several researches on this topic are being developed. This paper focuses on the identification stage of the orders p of these models. Nowadays, the identification is based on evaluating the significance of the coefficients of the partial autocorrelation function (PACF), based on the asymptoticresults of Quenouille. The purpose of this study is on the application of the computer-intensive Bootstraptechnique to estimate the significance of such coefficients. The results show that identification via Bootstrap is considerably more parsimonious, leading to the identification of lower orders in most cases andcorroborating some points raised in previous studies on the traditional approach.