Regression tree model for prediction of demand with heterogeneity and censorship

In this research we analyze a new approach for prediction of demand. In the studied market of performing arts the observed demand is limited by capacity of the house. Then one needs to account for demand censorship to obtain unbiased estimates of demand function parameters. The presence of consumer segments with different purposes of going to the theater and willingness-to-pay for performance and ticket characteristics causes a heterogeneity in theater demand. We propose an estimator for prediction of demand that accounts for both demand censorship and preferences heterogeneity. The estimator is based on the idea of classification and regression trees and bagging prediction aggregation extended for prediction of censored data. Our algorithm predicts and combines predictions for both discrete and continuous parts of censored data. We show that our estimator performs better in terms of prediction accuracy compared with estimators which account either for censorship or heterogeneity only. The proposed approach is helpful for finding product segments and optimal price setting.     

A robust neural network filter for electricity demand prediction

This paper is concerned with one-day-ahead hourly predictions of electricity demand for Puget Power, a local electricity utility for the Seattle area. Standard modelling techniques, including neural networks, will fail when the assumptions of the model are violated. It is demonstrated that typical modelling assumptions such as no outliers or level shifts are incorrect for electric power demand time series. A filter which removes or lessens the significance of outliers and level shifts is demonstrated. This filter produces ‘clean data’ which is used as the basis for future robust predictions. The robust predictions are shown to be better than non-robust counterparts on electricity load data. The outliers identified by the filter are shown to correspond with suspicious data. Finally, the estimated level shifts are in agreement with the belief that load growth is taking place year to year.     

Modelling peak electricity demand

This paper estimates a forecasting equation for the hourly peak electricity demand one day in the future. The models incorporate deterministic influences such as holidays, stochastic influences such as average loads by building bivariate models, and exogenous influences such as the weather which is given a careful non-linear formulation. Out-of-sample comparisons are made using an additional year of data.     

Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003–2008?

Recently developed structural models of the global crude oil market imply that the surge in the real price of oil between mid 2003 and mid 2008 was driven by repeated positive shocks to the demand for all industrial commodities, reflecting unexpectedly high growth mainly in emerging Asia. We evaluate this proposition using an alternative data source and a different econometric methodology. Rather than inferring demand shocks from an econometric model, we utilize a direct measure of global demand shocks based on revisions of professional real gross domestic product (GDP) growth forecasts. We show that forecast surprises during 2003–2008 were associated primarily with unexpected growth in emerging economies (in conjunction with much smaller positive GDP‐weighted forecast surprises in the major industrialized economies), that markets were repeatedly surprised by the strength of this growth, that these surprises were associated with a hump‐shaped response of the real price of oil that reaches its peak after 12–16 months, and that news about global growth predict much of the surge in the real price of oil from mid 2003 until mid 2008 and much of its subsequent decline. Copyright © 2012 John Wiley & Sons, Ltd.     

Forecasting of electricity price through a functional prediction of sale and purchase curves

This work proposes a new approach for the prediction of the electricity price based on forecasting aggregated purchase and sale curves. The basic idea is to model the hourly purchase and the sale curves, to predict them and to find the intersection of the predicted curves in order to obtain the predicted equilibrium market price and volume. Modeling and forecasting of purchase and sale curves is performed by means of functional data analysis methods. More specifically, parametric (FAR) and nonparametric (NPFAR) functional autoregressive models are considered and compared to some benchmarks. An appealing feature of the functional approach is that, unlike other methods, it provides insights into the sale and purchase mechanism connected with the price and demand formation process and can therefore be used for the optimization of bidding strategies. An application to the Italian electricity market (IPEX) is also provided, showing that NPFAR models lead to a statistically significant improvement in the forecasting accuracy.     

A constrained least squares method for estimating the effects of an unknown monotonically intervening factor

A constrained least squares method is developed for the estimation of the effects of an unknown intervening causal factor in regression analysis, when the unknown factor shifts the regression hyperplane monotonically upwards (downwards) over time. As an illustration, we estimate the price elasticity of cigarettes in the USA and the systematic shifts of the demand curve for cigarettes during the time period 1964-86 (these shifts presumably reflecting the heightened awareness of the general public of the potential dangers of smoking).     

Metering and modelling residential end-use electricity load curves

In most electricity systems the residential sector is one of the main contributors to the system peak. This makes it important to know how different residential end uses, such as space heating or cooking, contribute to the system load curve at the time of system peak and also at other times of the day. In this paper we discuss the estimation of residential end-use load curves for the state of New South Wales in Australia. Half-hourly readings were taken for 15 months on the total load and a range of end-use loads of 250 households. Information was sought on 16 different end uses, while eight metering channels were available for each household. We describe the optimal design procedure used to determine which end uses to meter in each household. The econometric model used for estimating the end-use load curves integrates a conditional demand analysis (CDA) of the total load readings for the household with the readings on all the directly metered end uses. Our integrated approach achieves impressive gains in efficiency over the conventional approach to estimating end-use loads. The paper concludes with an illustration of how end-use load curves can be used to simulate a variety of policy options.     

Space‐Time Model versus VAR Model: Forecasting Electricity demand in Japan

This paper examined the forecasting performance of disaggregated data with spatial dependency and applied it to forecasting electricity demand in Japan. We compared the performance of the spatial autoregressive ARMA (SAR‐ARMA) model with that of the vector autoregressive (VAR) model from a Bayesian perspective. With regard to the log marginal likelihood and log predictive density, the VAR(1) model performed better than the SAR‐ARMA( 1,1) model. In the case of electricity demand in Japan, we can conclude that the VAR model with contemporaneous aggregation had better forecasting performance than the SAR‐ARMA model. Copyright © 2011 John Wiley & Sons, Ltd.     

Do professional forecasters believe in the Phillips curve? evidence from the G7 countries

This paper uses monthly survey data for the G7 countries for the time period 1989–2007 to explore the link between expectations on nominal wages, prices and unemployment rates as suggested by the wage and price Phillips curves. Four major findings stand out. First, we find that survey participants trust in both types of Phillips curve relationships. Second, we find evidence in favor of nonlinearities in the price Phillips curve. Third, we take into account a kink in the price Phillips curve to indicate that the slope of the Phillips curve differs during the business cycle. We find strong evidence of this feature in the data which confirms recent theoretical discussions. Fourth, we employ our data to the expectations‐augmented Phillips curve model. The results suggest that professional forecasters adopt this model when forecasting macroeconomic variables. Copyright © 2010 John Wiley & Sons, Ltd.     

A new approach to forecasting intermittent demand based on the mixed zero-truncated Poisson model

This paper proposes a new approach to forecasting intermittent demand by considering the effects of external factors. We classify intermittent demand data into two parts—zero value and nonzero value—and fit nonzero values into a mixed zero-truncated Poisson model. All the parameters in this model are obtained by an EM algorithm, which regards external factors as independent variables of a logistic regression model and log-linear regression model. We then calculate the probability of occurrence of zero value at each period and predict demand occurrence by comparing it with critical value. When demand occurs, we use the weighted average of the mixed zero-truncated Poisson model as predicted nonzero demands, which are combined with predicted demand occurrences to form the final forecasting demand series. Two performance measures are developed to assess the forecasting methods. By presenting a case study of electric power material from the State Grid Shanghai Electric Power Company in China, we show that our approach provides greater accuracy in forecasting than the Poisson model, the hurdle shifted Poisson model, the hurdle Poisson model, and Croston's method.     

Modelling the impact of temperature on electricity consumption in the Eastern Province of Saudi Arabia

An econometric model is developed to forecast electricity consumption and study the impact of ambient temperature, expressed in terms of degree days (DDs), on consumption in the Eastern Province of Saudi Arabia. It is apparent that temperature plays an important role in the demand for electricity. The relationship between the behaviour of electricity consumption and temperature expressed in DDs is explored.     

旅游需求预测方法的比较分析

需求预测是旅游计划管理的一项重要工作。旅游需求预测对于旅游地规划和建设旅游基础设施,组织产品和提供旅游者服务是一个基础性的前期工作。然而,旅游产品的易逝性和不可贮存之特征,对旅游需求预测提出了更高的要求。本文试图通过总结和评价自20世纪中期以来旅游需求预测的方法,确立一个适合于中国旅游发展的新思路。     

A detailed look at crude oil price volatility prediction using macroeconomic variables

We investigate whether crude oil price volatility is predictable by conditioning on macroeconomic variables. We consider a large number of predictors, take into account the possibility that relative predictive performance varies over the out-of-sample period, and shed light on the economic drivers of crude oil price volatility. Results using monthly data from 1983:M1 to 2018:M12 document that variables related to crude oil production, economic uncertainty and variables that either describe the current stance or provide information about the future state of the economy forecast crude oil price volatility at the population level 1 month ahead. On the other hand, evidence of finite-sample predictability is very weak. A detailed examination of our out-of-sample results using the fluctuation test suggests that this is because relative predictive performance changes drastically over the out-of-sample period. The predictive power associated with the more successful macroeconomic variables concentrates around the Great Recession until 2015. They also generate the strongest signal of a decrease in the price of crude oil towards the end of 2008.     

An interpretation of the non-symmetric responding logistic model in terms of price and experience effects

This paper presents an alternative derivation and a generalization of the non-symmetric responding logistic model of Easingwood, Mahajan and Muller (1981) based upon a combination of experience curve and price elasticity effects.     

Gaussian processes for daily demand prediction in tourism planning

This study proposes Gaussian processes to forecast daily hotel occupancy at a city level. Unlike other studies in the tourism demand prediction literature, the hotel occupancy rate is predicted on a daily basis and 45 days ahead of time using online hotel room price data. A predictive framework is introduced that highlights feature extraction and selection of the independent variables. This approach shows that the dependence on internal hotel occupancy data can be removed by making use of a proxy measure for hotel occupancy rate at a city level. Six forecasting methods are investigated, including linear regression, autoregressive integrated moving average and recent machine learning methods. The results indicate that Gaussian processes offer the best tradeoff between accuracy and interpretation by providing prediction intervals in addition to point forecasts. It is shown how the proposed framework improves managerial decision making in tourism planning.     

The impact on the performance of growth curve models of changes in parameter re-estimation period and other factors

A simulation model of a real electricity supply undertaking was used to provide a financial performance measure for growth curve forecasting models. The impact on financial performance was determined when changes were made in (1) the method of estimating the model parameters, (2) the period between re-estimations, (3) the growth curve fitted and (4) the amount of smoothing of the demand time-series. The response to variation of the parameter review period was found to behave surprisingly, in that it exhibited different signs for two different estimation methods. Changes in re-estimation period explained somewhat more of the variation in performance than did a change in growth curve. Correcting the demand series for conditions which were known to be abnormal improved performance.     

Household projection methods

"The role of household projections as a basis for forecasts of households at [the] national and sub-national level is discussed and a number of criteria for such projections are outlined. The projection method used by the Department of the Environment [in the United Kingdom] is examined in the context of these criteria and it is concluded that it is both practical and robust. However, it is open to criticism, first because of its failure to make the best use of the available data and of theoretical knowledge, and secondly because of its 'black box' nature. An alternative two-stage strategy is developed. The first stage involves constructing projections using a new curve-fitting method which takes account of within cohort life-cycle headship rate changes. The second is a method of analysing the resulting projections by modelling transition rates between different household states. Worked examples of both methods are presented."     

Forecasting with money demand functions: the UK case

Money demand functions have long been known to be frequently subject to structural change. Since their use for optimal monetary policy design is basically a forecasting exercise, it is crucial to analyse the effect of time instability on the quality of their forecasts. We discuss in this paper whether instability of demand for money functions precludes their use for policy experiments, analysing a 1963–84 sample for the UK which has been widely used in the literature. © 1998 John Wiley & Sons, Ltd.     

Combination of forecasts in electricity demand prediction

Accurate demand prediction is of great importance in the electricity supply industry. Electricity cannot be stored, and generating plant must be scheduled well in advance to meet future demand. Up to now, where online information about external conditions is unavailable, time series methods on the historical demand series have been used for short-term demand prediction. These have drawbacks, both in their sensitivity to changing weather conditions and in their poor modelling of the daily/weekly business cycles. To overcome these problems a framework has been constructed whereby forecasts from different prediction methods and different forecasting origins can be selected and combined, solely on the basis of recent forecasting performance, with no a priori assumptions of demand behaviour. This added flexibility in univariate forecasting provides a significant improvement in prediction accuracy.     

Assessing inefficiency in the s&p 500 futures market

We analyse the price movement of the S&P 500 futures market for violations of the efficient market hypothesis on a short-term basis. To assess market inefficiency we construct a model and find that the returns, i.e. the difference in the logarithm of closing prices on consecutive days, exhibit the usual conditional heteroscedasticity behaviour typical of long series of financial data. To account for this non-linear behaviour we scale the returns by a volatility factor which depends on the daily high, low, and closing price. The rescaled series, which may be interpreted as the trend-countertrend component of the time series, is modelled using Box and Jenkins techniques. The resulting model is an ARMA(1,1). The scale factors are assumed to form a time series and are modelled using a semi-non-parametric method which avoids the restrictive assumptions of most ARCH or GARCH models. Using the combined model we perform 1000 simulations of market data, each simulation comprising 250 days (approximately one year). We then formulate a naive trading strategy which is based on the ratio of the one-day-ahead expected return to its one-day-ahead expected conditional standard deviation. The trading strategy has four adjustable parameters which are set to maximize profits for the simulation data. Next, we apply the trading strategy to one year of recent out-of-sample data. Our conclusion is that the S&P 500 futures market exhibits only slight inefficiencies, but that there exist, in principle, better trading strategies which take account of risk than the benchmark strategy of buy-and-hold. We have also constructed a linear model for the return series. Using the linear model, we have simulated returns and determined the optimum values for the adjustable parameters of the trading strategy. In this case, the optimum trading strategy is the same as the benchmark strategy, buy-and-hold. Finally, we have compared the profitability of the optimized trading strategy, based on the non-linear model, to three ad hoc trading strategies using the out-of-sample data. The three ad hoc strategies are more profitable than the optimized strategy.