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Research on forecasting is effectively limited to forecasts that are expressed with clarity; which is to say that the forecasted event must be sufficiently well-defined so that it can be clearly resolved whether or not the event occurred and forecasts certainties are expressed as quantitative probabilities. When forecasts are expressed with clarity, then quantitative measures (scoring rules, calibration, discrimination, etc.) can be used to measure forecast accuracy, which in turn can be used to measure the comparative accuracy of different forecasting methods. Unfortunately most real world forecasts are not expressed clearly. This lack of clarity extends to both the description of the forecast event and to the use of vague language to express forecast certainty. It is thus difficult to assess the accuracy of most real world forecasts, and consequently the accuracy the methods used to generate real world forecasts. This paper addresses this deficiency by presenting an approach to measuring the accuracy of imprecise real world forecasts using the same quantitative metrics routinely used to measure the accuracy of well-defined forecasts. To demonstrate applicability, the Inferred Probability Method is applied to measure the accuracy of forecasts in fourteen documents examining complex political domains. Key words: inferred probability, imputed probability, judgment-based forecasting, forecast accuracy, imprecise forecasts, political forecasting, verbal probability, probability calibration.

The objective of this paper is to assess the accuracy of air temperature and precipitation monthly and seasonal forecasts generated for the territory of Lithuania using the NOAA’s Climate Forecast System, version 2 (CFSv2) and to determine the atmospheric circulation conditions present at the time of initialization of the respective forecasts. The air temperature and precipitation data are obtained from three-month mean and monthly mean spatial anomalies during the period between 2012 and 2019. The accuracy of forecasts was performed in accordance with three criteria: range, state and the absolute error of the respective predicted anomaly. The study has shown that forecasts initialized 0–20 days in advance of the target month or season tend to be the most skilful. The accuracy of CFSv2 forecasts may be significantly impacted by the initial atmospheric circulation conditions present during the generation thereof. The study determined which phases of Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) and which circulation types according to the Hess-Brezowsky classification are favourable/unfavourable for the monthly and seasonal forecasting of air temperature and precipitation.

Weather forecasting is important for science and society. At present, the most accurate forecast system is the numerical weather prediction (NWP) method, which represents atmospheric states as discretized grids and numerically solves partial differential equations that describe the transition between those states 1 . However, this procedure is computationally expensive. Recently, artificial-intelligence-based methods 2 have shown potential in accelerating weather forecasting by orders of magnitude, but the forecast accuracy is still significantly lower than that of NWP methods. Here we introduce an artificial-intelligence-based method for accurate, medium-range global weather forecasting. We show that three-dimensional deep networks equipped with Earth-specific priors are effective at dealing with complex patterns in weather data, and that a hierarchical temporal aggregation strategy reduces accumulation errors in medium-range forecasting. Trained on 39 years of global data, our program, Pangu-Weather, obtains stronger deterministic forecast results on reanalysis data in all tested variables when compared with the world’s best NWP system, the operational integrated forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF) 3 . Our method also works well with extreme weather forecasts and ensemble forecasts. When initialized with reanalysis data, the accuracy of tracking tropical cyclones is also higher than that of ECMWF-HRES. Three-dimensional deep neural networks can be trained to forecast global weather patterns, including extreme weather, with accuracy greater than or equal to that of the best numerical weather prediction models.

Purpose Property market models have the overriding aim of predicting reasonable estimates of key dependent variables (demand, supply, rent, yield, vacancy and net absorption rate). These can be based on independent drivers of core property and economic activities. Accurate predictions can only be conducted when ample quantitative data are available with fewer uncertainties. However, a broad-fronted social, technical and ecological evolution can throw up sudden, unexpected shocks that result in the econometric outputs sceptical to unknown risk factors. Therefore, the purpose of this paper is to evaluate Australian office market forecast accuracy and to determine whether the forecasts capture extreme downside risk events. Design/methodology/approach This study follows a quantitative research approach, using secondary data analysis to test the accuracy of economists’ forecasts. The forecast accuracy evaluation encompasses the measurement of economic and property forecasts under the following phases: testing for the forecast accuracy; analysing outliers of forecast errors; and testing of causal relationships. Forecast accuracy measurement incorporates scale independent metrics that include Theil’s U values (U1 and U2) and mean absolute scaled error. Inter-quartile range rule is used for the outlier analysis. To find the causal relationships among variables, the time series regression methodology is utilised, including multiple regression analysis and Granger causality developed under the vector auto regression (VAR). Findings The credibility of economic and property forecasts was questionable around the period of the Global Financial Crisis (GFC); a significant man-made Black Swan event. The forecast accuracy measurement highlighted rental movement and net absorption forecast errors as the critical inaccurate predictions. These key property variables are explained by historic information and independent economic variables. However, these do not explain the changes when error time series of the variables were concerned. According to VAR estimates, all property variables have a significant causality derived from the lagged values of Australian S&P/ASX 200 (ASX) forecast errors. Therefore, lagged ASX forecast errors could be used as a warning signal to adjust property forecasts. Research limitations/implications Secondary data were obtained from the premier Australian property markets: Canberra, Sydney, Brisbane, Adelaide, Melbourne and Perth. A limited ten-year timeframe (2001-2011) was used in the ex-post analysis for the comparison of economic and property variables. Forecasts ceased from 2011, due to the discontinuity of the Australian Financial Review quarterly survey of economists; the main source of economic forecast data. Practical implications The research strongly recommended naïve forecasts for the property variables, as an input determinant in each office market forecast equation. Further, lagged forecast errors in the ASX could be used as a warning signal for the successive property forecast errors. Hence, data adjustments can be made to ensure the accuracy of the Australian office market forecasts. Originality/value The paper highlights the critical inaccuracy of the Australian office market forecasts around the GFC. In an environment of increasing incidence of unknown events, these types of risk events should not be dismissed as statistical outliers in real estate modelling. As a proactive strategy to improve office market forecasts, lagged ASX forecast errors could be used as a warning signal. This causality was mirrored in rental movements and total vacancy forecast errors. The close interdependency between rents and vacancy rates in the forecasting process and the volatility in rental cash flows reflects on direct property investment and subsequently on the ASX, is therefore justified.

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.

Warm-sector heavy rainfall in southern China refers to the heavy rainfall that occurs within the warm sector hundreds of kilometers south of a front or without a front during April–June, characterized by poor predictability and a close relationship with low-level jets (LLJs). Based on 45 warm-sector heavy rainfall episodes in 2013 and 2014 in southern China, this study examines their general characteristics and evaluates the performance of convection-permitting WRF Model simulations from an LLJ perspective. The results show that 64% of the warm-sector heavy rainfall episodes are associated with an LLJ (LLJ type) and 36% are not (no-LLJ type). The LLJ type is distinct from the no-LLJ type, with large rainfall accumulation along the coastal area. It is more common for LLJs to occur at both 800 and 925 hPa in the LLJ type, where there is a wide 800-hPa LLJ west of Guangdong Province and two 925-hPa LLJs over Beibu Gulf and the South China Sea (SCS). The coastal convergence associated with the terminus of the LLJ on 925 hPa is conducive to the coastal rainfall. WRF generally presents lower QPF skill in the LLJ type than in the no-LLJ type, due to the severe underestimation of coastal rainfall. The QPF skill of the LLJ type is significantly correlated with the forecast accuracy of LLJs, especially at 925 hPa. The north bias of the simulated LLJ on 925 hPa over the SCS and the associated overestimation of wind speed below ~900 hPa over the inland region weaken the coastal convergence and eventually lead to the underestimation in coastal precipitation.

Firms often make mistakes, from simple manufacturing overruns all the way to catastrophic blunders. However, there is considerable heterogeneity in the nature of corporate responses when faced with evidence that an error has taken place, and, therefore, in the likelihood that such errors will reoccur in the future. In this paper, we explore an important but understudied influence on firms' responses to corrective feedback— a CEO's level of overconfidence. Using multiple distinct measures of overconfidence and the empirical context of voluntary corporate earnings forecasts, we find strong, robust evidence that firms led by overconfident CEOs are less responsive to corrective feedback in improving management forecast accuracy. We further show that this relationship is moderated by prior forecast error valence, time horizon, and managerial discretion.

This paper studies information sharing in a distribution channel where the manufacturer possesses better demand-forecast information than the downstream retailer. We examine three information-sharing formats: no information sharing (i.e., the manufacturer ex ante commits to not sharing its forecast), voluntary information sharing (i.e., the manufacturer makes the sharing decision ex post after receiving the forecast), and mandatory information sharing (i.e., the manufacturer is mandated to share its forecast). We characterize the equilibrium outcomes under the three sharing formats and investigate the firms’ preferences regarding these formats. It is shown that when the retailer is risk-neutral, both firms are indifferent between voluntary and mandatory sharing. Among the three formats, ex ante, the retailer prefers the no-sharing format whereas the manufacturer prefers the mandatory-sharing format. In addition, we find that a more accurate forecast benefits both firms under voluntary- and mandatory-sharing formats, but may hurt both firms under the no-sharing format. Finally, we show that risk aversion plays a critical role in the firms’ sharing decisions and the impact of forecast accuracy. Specifically, when the retailer is risk-averse, the manufacturer may prefer the no-sharing format over the voluntary-sharing format, and improving forecast accuracy may hurt both firms even under voluntary sharing.

Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

Real estate markets are inherently dynamic, influenced by economic fluctuations, policy changes and socio-demographic shifts, often leading to emergence of anomalous—regions, where market behavior significantly deviates from expected trends. Traditional forecasting models struggle to handle such anomalies, resulting in higher errors and reduced prediction stability. In order to address this challenge, we propose EGCN, a novel cluster-specific forecasting framework that first detects and clusters anomalous regions separately from normal regions, and then applies forecasting models. This structured approach enables predictive models to treat normal and anomalous regions independently, leading to enhanced market insights and improved forecasting accuracy. Our evaluations on the UK, USA, and Australian real estate market datasets demonstrates that the EGCN achieves the lowest error both anomaly-free (baseline) methods and alternative anomaly detection methods, across all forecasting horizons (12, 24, and 48 months). In terms of anomalous region detection, our EGCN identifies 182 anomalous regions in Australia, 117 in the UK and 34 in the US, significantly more than the other competing methods, indicating superior sensitivity to market deviations. By clustering anomalies separately, forecasting errors are reduced across all tested forecasting models. For instance, when applying Neural Hierarchical Interpolation for Time Series Forecasting, the EGCN improves accuracy across forecasting horizons. In short-term forecasts (12 months), it reduces MSE from 1.3 to 1.0 in the US, 9.7 to 6.4 in the UK and 2.0 to 1.7 in Australia. For mid-term forecasts (24 months), EGCN achieves the lowest errors, lowering MSE from 3.1 to 2.3 (US), 14.2 to 9.0 (UK), and 4.5 to 4.0 (Australia). Even in long-term forecasts (48 months), where error accumulation is common, EGCN remains stable; decreasing MASE from 6.9 to 5.3 (US), 12.2 to 8.5 (UK), and 16.0 to 15.2 (Australia), highlighting its robustness over extended periods. These results highlight how separately clustering anomalies allows forecasting models to better capture distinct market behaviors, ensuring more precise and risk-adjusted predictions.