Short-term forecasting of crime

The major question investigated is whether it is possible to accurately forecast selected crimes 1 month ahead in small areas, such as police precincts. In a case study of Pittsburgh, PA, we contrast the forecast accuracy of univariate time series models with naïve methods commonly used by police. A major result, expected for the small-scale data of this problem, is that average crime count by precinct is the major determinant of forecast accuracy. A fixed-effects regression model of absolute percent forecast error shows that such counts need to be on the order of 30 or more to achieve accuracy of 20% absolute forecast error or less. A second major result is that practically any model-based forecasting approach is vastly more accurate than current police practices. Holt exponential smoothing with monthly seasonality estimated using city-wide data is the most accurate forecast model for precinct-level crime series.     

Forecasting the New York State economy: The coincident and leading indicators approach

Currently there are no reliable summary indicators of the economic and fiscal condition of states and localities. This deficiency has hampered the efforts of policy makers at the sub-national level to monitor changes in the economic environment and predict how those changes will impact the fiscal health of governments. This paper attempts to fill this analytical vacuum by providing summary indicators of economic and fiscal health for New York State. The models developed are based on the single-index methodology developed by Stock and Watson [(1991). A probability model of the coincident economic indicators. In K. Lahiri and G. H. Moore (eds.), Leading economic indicators: new approaches and forecasting records (pp. 63–85). New York: Cambridge University Press]. This approach allows us to date New York business cycles and compare local cyclical behavior with the nation as a whole. We develop a leading index of economic indicators which predicts future movements in the coincident indicator. The Stock and Watson approach is used to create a fiscal indicator which acts as a summary indicator of revenue performance for New York. In addition, we explore the ability of our economic indicator series to predict future changes in state revenues. We find that changes in the leading indicator series have significant predictive power in forecasting changes in our revenue index.     

Forecasting and explaining aggregate consumer credit delinquency behaviour

We model aggregate delinquency behaviour for consumer credit (including credit card loans and other consumer loans) and for residential real estate loans using data up until 2008. We test for cointegrating relationships and then estimate short run error correction models. We find evidence to support the portfolio explanations of declines in credit quality for consumer and for real estate loans, but support for the reduced stigma explanation was restricted to real estate loans. Evidence supportive of household-level explanations of irrational borrowing and unexpected net income shocks was found for consumer and real estate loans, but evidence of strategic default was restricted to the volume of consumer loans and real estate loans, and not for credit cards. We also found that the error correction model gave forecasts of the volume of delinquent consumer debt which were of an accuracy comparable to that of an ARIMA model.     

Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy

This paper shows that forecasting accuracy can be improved through better estimation of seasonal factors under conditions for which relatively simple methods are preferred, such as relatively few historical data, noisy data, and/or a large number of series to be forecasted. In such situations, the preferred method of seasonal adjustment is often ratio-to-moving-averages (classical) decomposition. This paper proposes two shrinkage estimators to improve the accuracy of classical decomposition seasonal factors. In a simulation study, both of the proposed estimators provided consistently greater accuracy than classical decomposition, with the improvement sometimes being dramatic. The performances of the two estimators depended on characteristics of the series, and guidelines were developed for choosing one of them under a given set of conditions. For a set of monthly, M-competition series, greater forecasting accuracy was achieved when either of the proposed methods was used for seasonal adjustment rather than classical decomposition, and the greatest accuracy was achieved by following the guidelines for choosing a method.     

Forecasting the US housing market

The recent housing market boom and bust in the United States illustrates that real estate returns are characterized by short-term positive serial correlation and long-term mean reversion to fundamental values. We develop an econometric model that includes these two components, but with weights that vary dynamically through time depending on recent forecasting performances. The smooth transition weighting mechanism can assign more weight to positive serial correlation in boom times, and more weight to reversal to fundamental values during downturns. We estimate the model with US national house price index data. In-sample, the switching mechanism significantly improves the fit of the model. In an out-of-sample forecasting assessment the model performs better than competing benchmark models.     

Forecasting electricity prices using bid data

Market liberalization and the expansion of variable renewable energy sources in power systems have made the dynamics of electricity prices more uncertain, leading them to show high volatility with sudden, unexpected price spikes. Thus, developing more accurate price modeling and forecasting techniques is a challenge for all market participants and regulatory authorities. This paper proposes a forecasting approach based on using auction data to fit supply and demand electricity curves. More specifically, we fit linear (LinX-Model) and logistic (LogX-Model) curves to historical sale and purchase bidding data from the Iberian electricity market to estimate structural parameters from 2015 to 2019. Then we use time series models on structural parameters to predict day-ahead prices. Our results provide a solid framework for forecasting electricity prices by capturing the structural characteristics of markets.     

Forecasting ATM cash demands using a local learning model of cerebellar associative memory network

Forecasting cash demands at automatic teller machines (ATMs) is challenging, due to the heteroskedastic nature of such time series. Conventional global learning computational intelligence (CI) models, with their generalized learning behaviors, may not capture the complex dynamics and time-varying characteristics of such real-life time series data efficiently. In this paper, we propose to use a novel local learning model of the pseudo self-evolving cerebellar model articulation controller (PSECMAC) associative memory network to produce accurate forecasts of ATM cash demands. As a computational model of the human cerebellum, our model can incorporate local learning to effectively model the complex dynamics of heteroskedastic time series. We evaluated the forecasting performance of our PSECMAC model against the performances of current established CI and regression models using the NN5 competition dataset of 111 empirical daily ATM cash withdrawal series. The evaluation results show that the forecasting capability of our PSECMAC model exceeds that of the benchmark local and global-learning based models.     

Forecasting bulk prices of Bordeaux wines using leading indicators

Agricultural price forecasting has been being abandoned progressively by researchers ever since the development of large-scale agricultural futures markets. However, as with many other agricultural goods, there is no futures market for wine. This paper draws on the agricultural prices forecasting literature to develop a forecasting model for bulk wine prices. The price data include annual and monthly series for various wine types that are produced in the Bordeaux region. The predictors include several leading economic indicators of supply and demand shifts. The stock levels and quantities produced are found to have the highest predictive power. The preferred annual and monthly forecasting models outperform naive random walk forecasts by 27.1% and 3.4% respectively; their mean absolute percentage errors are 2.7% and 3.4% respectively. A simple trading strategy based on monthly forecasts is estimated to increase profits by 3.3% relative to a blind strategy that consists of always selling at the spot price.     

COVID-19: Forecasting confirmed cases and deaths with a simple time series model

Forecasting the outcome of outbreaks as early and as accurately as possible is crucial for decision-making and policy implementations. A significant challenge faced by forecasters is that not all outbreaks and epidemics turn into pandemics, making the prediction of their severity difficult. At the same time, the decisions made to enforce lockdowns and other mitigating interventions versus their socioeconomic consequences are not only hard to make, but also highly uncertain. The majority of modeling approaches to outbreaks, epidemics, and pandemics take an epidemiological approach that considers biological and disease processes. In this paper, we accept the limitations of forecasting to predict the long-term trajectory of an outbreak, and instead, we propose a statistical, time series approach to modelling and predicting the short-term behavior of COVID-19. Our model assumes a multiplicative trend, aiming to capture the continuation of the two variables we predict (global confirmed cases and deaths) as well as their uncertainty. We present the timeline of producing and evaluating 10-day-ahead forecasts over a period of four months. Our simple model offers competitive forecast accuracy and estimates of uncertainty that are useful and practically relevant.     

News will tell: Forecasting foreign exchange rates based on news story events in the economy calendar

The paper proposes a novel approach to predict intraday directional-movements of currency-pairs in the foreign exchange market based on news story events in the economy calendar. Prior work on using textual data for forecasting foreign exchange market developments does not consider economy calendar events. We consider a rich set of text analytics methods to extract information from news story events and propose a novel sentiment dictionary for the foreign exchange market. The paper shows how news events and corresponding news stories provide valuable information to increase forecast accuracy and inform trading decisions. More specifically, using textual data together with technical indicators as inputs to different machine learning models reveals that the accuracy of market predictions shortly after the release of news is substantially higher than in other periods, which suggests the feasibility of news-based trading. Furthermore, empirical results identify a combination of a gradient boosting algorithm, our new sentiment dictionary, and text-features based-on term frequency weighting to offer the most accurate forecasts. These findings are valuable for traders, risk managers and other consumers of foreign exchange market forecasts and offer guidance how to design accurate prediction systems.     

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

We extend neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well-performing deep learning model, extending its capabilities by including exogenous variables and allowing it to integrate multiple sources of useful information. To showcase the utility of the NBEATSx model, we conduct a comprehensive study of its application to electricity price forecasting tasks across a broad range of years and markets. We observe state-of-the-art performance, significantly improving the forecast accuracy by nearly 20% over the original NBEATS model, and by up to 5% over other well-established statistical and machine learning methods specialized for these tasks. Additionally, the proposed neural network has an interpretable configuration that can structurally decompose time series, visualizing the relative impact of trend and seasonal components and revealing the modeled processes’ interactions with exogenous factors. To assist related work, we made the code available in a dedicated repository.     

Forecasting and loss functions

This paper considers two problems of interpreting forecasting competition error statistics. The first problem is concerned with the importance of linking the error measure (loss function) used in evaluating a forecasting model with the loss function used in estimating the model. It is argued that because the variety of uses of any single forecast, such matching is impractical. Secondly, there is little evidence that matching would have any impact on comparative forecast performance, however measured. As a consequence the results of forecasting competitions are not affected by this problem. The second problem is concerned with the interpreting performance, when evaluated through M(ean) S(quare) E(rror). The authors show that in the Makridakis Competition, good MSE performance is solely due to performance on a small number of the 1001 series, and arises because of the effects of scale. They conclude that comparisons of forecasting accuracy based on MSE are subject to major problems of interpretation.     

Modelling and predicting recorded property crime trends in England and Wales—a retrospective

In 1999 the Home Office published, for the first time ever, 3-year ahead projections of property crime in England and Wales. The projections covered the period 1999–2001 and indicated strong upward pressure after five full years of falling crime. This pressure was generated by three factors: the number of young men in the general population, the state of the economy and the fact that property crime appeared to be well below its underlying trend level. The projections received a mixed response, with some agreeing that crime was set to rise while questioning the scale of any increase, to others who doubted the value of this type of econometric modelling. In fact, property crime did increase in 1999, although not at the rate suggested by the models—and indeed levels of burglary continued to fall. This paper addresses some of the reasons for this disparity as well as considering various criticisms of the Home Office approach.     

区间时间序列预测准确度探讨

区间时间序列在决策过程中提供重要的信息,特别是在经济发展、人口政策、规划管理或金融监管等方面,因此如何计算出预测区间的精确度成为一个重要议题。本文提出两种区间预测准确度分析的方法,通过估计预测结果的平均区间误差平方和及平均相对区间误差和,比较不同预测方法的优劣。并由预测区间与实际区间的重叠位置,充分说明预测方法所具有的有效性。这些分析预测区间准确度的方法,将为管理者提供更客观的决策空间。     

Forecasting with factor-augmented error correction models

As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over the standard ECM and FAVAR models. In particular, it uses a larger dataset than the ECM and incorporates the long-run information which the FAVAR is missing because of its specification in differences. In this paper, we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that FECM generally offers a higher forecasting precision relative to the FAVAR, and marks a useful step forward for forecasting with large datasets.     

Exploring the representativeness of the M5 competition data

The main objective of the M5 competition, which focused on forecasting the hierarchical unit sales of Walmart, was to evaluate the accuracy and uncertainty of forecasting methods in the field to identify best practices and highlight their practical implications. However, can the findings of the M5 competition be generalized and exploited by retail firms to better support their decisions and operation? This depends on the extent to which M5 data is sufficiently similar to unit sales data of retailers operating in different regions selling different product types and considering different marketing strategies. To answer this question, we analyze the characteristics of the M5 time series and compare them with those of two grocery retailers, namely Corporación Favorita and a major Greek supermarket chain, using feature spaces. Our results suggest only minor discrepancies between the examined data sets, supporting the representativeness of the M5 data.     

Criminal incident prediction using a point-pattern-based density model

Law enforcement agencies need crime forecasts to support their tactical operations; namely, predicted crime locations for next week based on data from the previous week. Current practice simply assumes that spatial clusters of crimes or “hot spots” observed in the previous week will persist to the next week. This paper introduces a multivariate prediction model for hot spots that relates the features in an area to the predicted occurrence of crimes through the preference structure of criminals. We use a point-pattern-based transition density model for space–time event prediction that relies on criminal preference discovery as observed in the features chosen for past crimes. The resultant model outperforms the current practices, as demonstrated statistically by an application to breaking and entering incidents in Richmond, VA.     

Interpreting the skill score form of forecast performance metrics

Performance measures of point forecasts are expressed commonly as skill scores, in which the performance gain from using one forecasting system over another is expressed as a proportion of the gain achieved by forecasting that outcome perfectly. Increasingly, it is common to express scores of probabilistic forecasts in this form; however, this paper presents three criticisms of this approach. Firstly, initial condition uncertainty (which is outside the forecaster’s control) limits the capacity to improve a probabilistic forecast, and thus a ‘perfect’ score is often unattainable. Secondly, the skill score forms of the ignorance and Brier scores are biased. Finally, it is argued that the skill score form of scoring rules destroys the useful interpretation in terms of the relative skill levels of two forecasting systems. Indeed, it is often misleading, and useful information is lost when the skill score form is used in place of the original score.