A quantile-boosting approach to forecasting gold returns

We use a quantile-boosting approach to compute out-of-sample forecasts of gold returns. The approach accounts for model uncertainty and model instability, and it allows forecasts to be computed under asymmetric loss functions. Different asymmetric loss functions represent different types of investors (optimists versus pessimists). We document how the performance of a simple trading rule varies across investor types.     

A data-driven approach to forecasting ground-level ozone concentration

The ability to forecast the concentration of air pollutants in an urban region is crucial for decision-makers wishing to reduce the impact of pollution on public health through active measures (e.g. temporary traffic closures). In this study, we present a machine learning approach applied to forecasts of the day-ahead maximum value of ozone concentration for several geographical locations in southern Switzerland. Due to the low density of measurement stations and to the complex orography of the use-case terrain, we adopted feature selection methods instead of explicitly restricting relevant features to a neighborhood of the prediction sites, as common in spatio-temporal forecasting methods. We then used Shapley values to assess the explainability of the learned models in terms of feature importance and feature interactions in relation to ozone predictions. Our analysis suggests that the trained models effectively learned explanatory cross-dependencies among atmospheric variables. Finally, we show how weighting observations helps to increase the accuracy of the forecasts for specific ranges of ozone’s daily peak values.     

An ensemble approach to GEFCom2017 probabilistic load forecasting

We present an ensembling approach to medium-term probabilistic load forecasting which ranked second out of 73 competitors in the defined data track of the GEFCom2017 qualifying match. In addition to being accurate, the ensemble method is highly scalable, due to the fact that it had to be applied to nine quantiles in ten zones and for six rounds. Candidate forecasts were generated using random settings for input data, covariates, and learning algorithms. The best candidate forecasts were averaged to create the final forecast, with the number of candidate forecasts being chosen based on their accuracy in similar validation periods.     

The theta model: a decomposition approach to forecasting

This paper presents a new univariate forecasting method. The method is based on the concept of modifying the local curvature of the time-series through a coefficient ‘Theta’ (the Greek letter θ), that is applied directly to the second differences of the data. The resulting series that are created maintain the mean and the slope of the original data but not their curvatures. These new time series are named Theta-lines. Their primary qualitative characteristic is the improvement of the approximation of the long-term behavior of the data or the augmentation of the short-term features, depending on the value of the Theta coefficient. The proposed method decomposes the original time series into two or more different Theta-lines. These are extrapolated separately and the subsequent forecasts are combined. The simple combination of two Theta-lines, the Theta=0 (straight line) and Theta=2 (double local curves) was adopted in order to produce forecasts for the 3003 series of the M3 competition. The method performed well, particularly for monthly series and for microeconomic data.     

Probabilistic forecasting of industrial electricity load with regime switching behavior

This paper suggests a novel inhomogeneous Markov switching approach for the probabilistic forecasting of industrial companies’ electricity loads, for which the load switches at random times between production and standby regimes. The model that we propose describes the transitions between the regimes using a hidden Markov chain with time-varying transition probabilities that depend on calendar variables. We model the demand during the production regime using an autoregressive moving-average (ARMA) process with seasonal patterns, whereas we use a much simpler model for the standby regime in order to reduce the complexity. The maximum likelihood estimation of the parameters is implemented using a differential evolution algorithm. Using the continuous ranked probability score (CRPS) to evaluate the goodness-of-fit of our model for probabilistic forecasting, it is shown that this model often outperforms classical additive time series models, as well as homogeneous Markov switching models. We also propose a simple procedure for classifying load profiles into those with and without regime-switching behaviors.     

Integrating qualitative and quantitative methods to enhance means-end approach

Conventionally adopted means-end chain (MEC) methodology uses product attributes, consequences and values to indicate consumption behavior hierarchies regarding specific products. These hierarchies are useful for elucidating consumer product knowledge and devise effective marketing strategies. In the MEC literature, the qualitative laddering scheme is the main approach used to identify the contents of consumer cognitive structures. However, MEC suffers limitations associated with the subjective research judgment. To overcome these weaknesses of MEC analysis, this work presents a novel laddering-matrix analysis (LMA) based on the quantitative matrix algorithm. The analytical results demonstrated that by integrating LMA and MEC it is possible to explore the information of the summary implication matrix without bias, thus providing extremely useful material for developing MEC computer software.     

A common-trends and error-correction approach to forecasting corporate bond returns

It is found that one unit root, common trend, is shared by the monthly price indices of the top four ratings of corporate bonds. Addition of an index of low-grade bonds to the vector time series results in two common trends. Consistent results are provided by dynamic factor analyses. The returns for the system of cointegrated indices can be represpnted by an error-correction model using past returns and cointegrating vectors. This model can provide more accurate forecasts than a common VAR that omits the cointegrating vectors. The common-trends analysis provides specific linear combinations, or cointegrating portfolios, of the index price levels that are stationary. The cointegrating portfolios associated with the two common trends have returns that are related to T-bill returns and unanticipated inflation.     

A gradient boosting approach to the Kaggle load forecasting competition

We describe and analyse the approach used by Team TinTin (Souhaib Ben Taieb and Rob J Hyndman) in the Load Forecasting track of the Kaggle Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem for a US utility with 20 geographical zones. The data available consisted of the hourly loads for the 20 zones and hourly temperatures from 11 weather stations, for four and a half years. For each zone, the hourly electricity loads for nine different weeks needed to be predicted without having the locations of either the zones or stations. We used separate models for each hourly period, with component-wise gradient boosting for estimating each model using univariate penalised regression splines as base learners. The models allow for the electricity demand changing with the time-of-year, day-of-week, time-of-day, and on public holidays, with the main predictors being current and past temperatures, and past demand. Team TinTin ranked fifth out of 105 participating teams.     

An economic-demographic approach to forecasting national and subnational birth rates

This paper conceptualizes and tests an economic-demographic fertility projection model, founded in the prevailing microeconomic theories of fertility behavior and embracing both economic and demographic determinants. Through a simulataneous system approach, the focus is on the linked decision areas for women concerning marriage, childbearing, employment, carreer and education. National age-race fertility rate projections are generated for the period of 1978–1985. An approach is explored whereby these can be adjusted to subnational levels and this is illustrated for the state of Illinois. The multivariate model appears to capture the key factors affecting fertility. It supports the Easterlin forecast that a turnaround from the steep decline of fertility rates during the past two decades will occur by the early 1980s. However the upturn will be relatively modest though sustained due to strong offsetting forces. The results justify further research on the potential and form of the economic-demographic approach to projecting the direction of fertility at the various geographic levels.     

The RWDAR model: A novel state-space approach to forecasting

This paper introduces the Random Walk with Drift plus AutoRegressive model (RWDAR) for time-series forecasting. Owing to the presence of a random walk plus drift term, this model shares some similarities with the Theta model of Assimakopoulos and Nikolopoulos (2000). However, the addition of a first-order autoregressive term in the state equation provides additional adaptability and flexibility. Indeed, it is shown that RWDAR tends to outperform the Theta model when forecasting both stationary and nearly non-stationary time series. This paper also proposes a simple estimation method for the RWDAR model based on the solution of the algebraic Riccati equation for the prediction error covariance of the state vector. Simulation results show that this estimator performs as well as the standard Kalman filter approach. Finally, using yearly data from the M3 and M4 competition datasets, it is found that RWDAR outperforms traditional forecasting methods.     

A feature engineering approach to wind power forecasting: GEFCom 2012

This paper provides detailed information about team Leustagos’ approach to the wind power forecasting track of GEFCom 2012. The task was to predict the hourly power generation at seven wind farms, 48 hours ahead. The problem was addressed by extracting time- and weather-related features, which were used to build gradient-boosted decision trees and linear regression models. This approach achieved first place in both the public and private leaderboards.     

A panel data approach to economic forecasting: The bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zero-mean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise, based upon data from a well known survey is also presented. Overall, these results show promise for the feasible bias-corrected average forecast.     

A qualitative dynamical modelling approach to capital accumulation in unregulated fisheries

Capital accumulation has been a major issue in fisheries economics over the last two decades, whereby the interaction of the fish and capital stocks were of particular interest. Because bio-economic systems are intrinsically complex, previous efforts in this field have relied on a variety of simplifying assumptions. The model presented here relaxes some of these simplifications. Problems of tractability are surmounted by using the methodology of qualitative differential equations (QDE). The theory of QDEs takes into account that scientific knowledge about particular fisheries is usually limited, and facilitates an analysis of the global dynamics of systems with more than two ordinary differential equations. The model is able to trace the evolution of capital and fish stock in good agreement with observed patterns, and shows that over-capitalization is unavoidable in unregulated fisheries.     

A new approach to estimating earnings forecasting models: Robust regression MM-estimation

This study focuses on the impact of model estimation methods on earnings forecast accuracy. Compared with an ordinary least squares (OLS) regression combined with winsorization, robust regression MM-estimation improves the earnings forecast accuracy of all the models examined, especially for those with more variables. My findings indicate that the impact of outliers on the OLS regression increases with the number of variables in the models, alerting researchers who use OLS regressions for forecasting. My findings explain the puzzling negative relationship between earnings forecast accuracy and the number of model variables in prior research. Moreover, I demonstrate the valuation implications of earnings forecasted using robust regression MM-estimation. This study contributes to earnings forecasting, valuation, and influential observation treatment in forecasting.     

Using a highly portable computer to gather qualitative social network data

We report on the use of a highly portable computer for collecting precise and accurate data concerning specific social network ties. Our experience confirmed that a highly portable computer, when loaded with a flexible database management program, is a useful tool for such a task. Data entry can be done during the interview and information retrieval can provide immediate and useful information for the respondents. The use of the computer with semistructured interviews changes the dynamics of the interview situation. A three-way interaction is created between the interviewers, the respondents and the information in the database. We found that there were great benefits from having two or three respondents in the session, and that it was desirable to have two interviewers.     

Big data from dynamic pricing: A smart approach to tourism demand forecasting

Suppliers of tourist services continuously generate big data on ask prices. We suggest using this information, in the form of a price index, to forecast the occupation rates for virtually any time-space frame, provided that there are a sufficient number of decision makers “sharing” their pricing strategies on the web. Our approach guarantees great transparency and replicability, as big data from OTAs do not depend on search interfaces and can facilitate intelligent interactions between the territory and its inhabitants, thus providing a starting point for a smart decision-making process. We show that it is possible to obtain a noticeable increase in the forecasting performance by including the proposed leading indicator (price index) into the set of explanatory variables, even with very simple model specifications. Our findings offer a new research direction in the field of tourism demand forecasting leveraging on big data from the supply side.     

Using weather ensemble predictions in electricity demand forecasting

Weather forecasts are an important input to many electricity demand forecasting models. This study investigates the use of weather ensemble predictions in electricity demand forecasting for lead times from 1 to 10 days ahead. A weather ensemble prediction consists of 51 scenarios for a weather variable. We use these scenarios to produce 51 scenarios for the weather-related component of electricity demand. The results show that the average of the demand scenarios is a more accurate demand forecast than that produced using traditional weather forecasts. We use the distribution of the demand scenarios to estimate the demand forecast uncertainty. This compares favourably with estimates produced using univariate volatility forecasting methods.