Correct Forecast Research Articles

Forecasting the parameters of non-stationary water flooding of oil deposits

Non-stationary flooding and water withdrawal with changing the direction of filtration flow as a secondary advanced recovery method allow enhancing oil deposit coverage by filtration processes, reducing or eliminating the amount of bypassed oil in productive formations. Process modelling should be provided for correct forecast about the efficiency of non-stationary formation stimulation. A formation model was designed for modelling cyclic water flooding. There are cross-flows between interlayers of reservoir robbing which have different porosity and permeability properties and unbalanced thrust in pore space. It is noted that when applying ordinary water flooding, high formation heterogeneity, higher than average oil viscosity and low permeability make it impossible to reach even average efficiency of oil pool development. The choice of a stimulation technique and its justification is an important step of planning in order to apply non-stationary stimulation. A possibility to apply non-stationary flooding efficiently for the analysed object has been established.

Forewarning Bankruptcy: An Indigenous Model for Pakistan

Predicting financial health of enterprises is crucial for companies and banks (who lend money to them), as well as to all stakeholders. State Bank of Pakistan (SBP) has taken measures to implement Basel Accord framework for minimizing credit risk since its implementation in 2005. In this regard, there was a need for the development of corporate financial distress and bankruptcy prediction model which is specific to Pakistani companies to serve as a reliable tool for the assessment of financial health. In this paper we have proposed a model after a comprehensive analysis for objective prediction of financial health of companies. The proposed model has been tested for a sample of one hundred companies from non-financial sector of PSX listed companies, among which fifty were bankrupt and fifty financially healthy. Twenty-nine financial and accounting variables were used as input to model development procedure. The results suggest that the proposed model is capable of making a correct forecast of financial health of these companies.

Higher order elicitability and Osband’s principle

A statistical functional, such as the mean or the median, is called elicitable if there is a scoring function or loss function such that the correct forecast of the functional is the unique minimizer of the expected score. Such scoring functions are called strictly consistent for the functional. The elicitability of a functional opens the possibility to compare competing forecasts and to rank them in terms of their realized scores. In this paper, we explore the notion of elicitability for multi-dimensional functionals and give both necessary and sufficient conditions for strictly consistent scoring functions. We cover the case of functionals with elicitable components, but we also show that one-dimensional functionals that are not elicitable can be a component of a higher order elicitable functional. In the case of the variance this is a known result. However, an important result of this paper is that spectral risk measures with a spectral measure with finite support are jointly elicitable if one adds the `correct' quantiles. A direct consequence of applied interest is that the pair (Value at Risk, Expected Shortfall) is jointly elicitable under mild conditions that are usually fulfilled in risk management applications.

STRATEGIC DISCLOSURE OF MEANINGFUL INFORMATION TO RIVAL

I study a model of strategic disclosure of a private signal to a rival in the presence of a payoff externality. In the model, two agents forecast the unknown true state of a future period. Specifically, the quality of the signal is also private information. Hence, which quality of signal is revealed for which incentive is the main question of interest. I show that, even when disclosure is costly, the revealing equilibrium, where an agent voluntarily reveals his signal, can exist and it should be a monotone equilibrium. Asymmetry between the penalty for an incorrect forecast and the reward for a correct forecast is a necessary condition for the existence of this revealing equilibrium. If the penalty is larger than the reward, the unique revealing equilibrium is a separating equilibrium, where only the low quality signal is disclosed in order to induce the rival's imitation. On the other hand, if the reward is much larger than the penalty, the unique revealing equilibrium is a pooling equilibrium, where the signal is always revealed in order to induce the rival's deviation. If the reward is not much larger than the penalty, no revealing equilibrium is robust to a costly disclosure. (JEL D81, D82)

Long-term changes of physicochemical parameters and benthos in Lake Qarun (Egypt): Can we make a correct forecast of ecosystem future?

Biotic changes and a salinity increase (from 13 to 40 g.L-1 ) occurred in Lake Qarun (Egypt) since 1901. Was salinity increasing a cause of observed biotic changes? To answer this question we used benthos as a model group. Benthos and water sampling was conducted in different seasons (2008–2013). Comparing our and literature data, we discuss the long-term trends and possible causes of benthos changes. Salinity reached 3 g.L-1 in middle of 19th century; and biotic changes caused by this were started. From middle 19th century to 1928 a biotic transformation was driven by the salinity increase; after 1928 a regular alien species introduction caused that a marine community formed. In 1970–2000 eutrophication played a main role in species composition changes. In 2014 ctenophore Mnemiopsis leidyi introduced in the lake; eutrophication, chemical pollution, and a population dynamics of this ctenophore may be main drivers of the ecosystem change now. Benthos biomass gradually decreased during interval 1975–2013 without any correlation with salinity change. A variety of other factors than salinity may be significant in determining the structure and dynamics of communities, and we conclude that we have a small chance to make a correct forecast of possible future ecosystem changes in Lake Qarun.

A Dynamic Modeling and Validation Framework for the Market Direction Prediction

There are many research papers talking about building various machine learning models to predict the market index. However, very few attention has been paid to effectively validating or calibrating the prediction results. The focus of this paper is to present a dynamic modeling and validation framework for the market direction prediction. The central idea is to calibrate the probabilistic prediction by estimating two conditional probabilities of correct forecast from the dynamic validation data set. The calibration method can be combined with any predictive model that generates probabilistic prediction of the market direction.

A Single Long Short-Term Memory Network can Predict Rainfall-Runoff at Multiple Timescales

Long Short-Term Memory Networks, otherwise known as LSTMs, have not been left out when it comes to applying them to daily discharge forecasts rather successfully. A good number of experimental cases, be as it may, need forecasts in a manner with a more granular time frame. Case in point, the correct forecast of brief but intense flooding apexes can bring about a difference with the capacity of saving lives in mass. Still, such climaxes have the capability of escaping the rough non-permanent resolve of daily forecasts. Nevertheless, when an LSTM data is naively learned on an hourly data basis, it entails a time-consuming process with lots of stages, which makes the training complex and computationally-cum financially costly. With this research, we suggest a pair of Multi-Time Scale LSTM or MTS-LSTM frameworks that collaboratively forecast a multiplicity of timescales inside a single model. This is done as they proceed with long-past investments in one non-permanent resolve and diversify into every timescale in order to arrive at more current input stages. For this, we carry out a test on these models on a total of 516 basins through the continental United States and standard in comparison with the United States National Water Model. Juxtaposed with naive forecasts that have distinctive LSTM for each time scale, multi-timescale designs will be computationally the more efficient party, suffering no loss of correctness. Outside the quality of predictions, the multiple-facing timescale has the capacity to process a variety of input variables at various timescales. That, in question, proves quite relevant when it comes to operational applications in which meteorological forcings’ lead time is contingent upon their non-permanent resolutions.

Improved tropical cyclone intensity and intensity spread prediction in bifurcation situations

Bifurcation or bi-modal tropical cyclone intensity forecasts may arise due to uncertainty in the timing of formation, timing and magnitude of rapid intensification periods, or track forecast uncertainty leading to landfall or non-landfall or leading to interaction with warm- or cold-ocean eddies. An objective technique is developed and tested to detect these intensity bifurcation situations in our weighted-analog intensity (WANI) forecasts that are based on the 10 best historical analogs to the Joint Typhoon Warning Center (JTWC) official track forecasts. About 19% of the overall sample of 1136 WANI forecasts in the western North Pacific during the 2010–2012 seasons met the criteria for a substantial intensity bifurcation situation. Using a hierarchical clustering technique, two clusters of the 10 best analogs are defined and separate WANI forecasts and intensity spreads are calculated for the two clusters. If an always perfect selection of the correct cluster WANI forecast of each bifurcation situation is made, a substantial improvement in the intensity mean absolute errors is achieved relative to the original WANI forecasts based on all 10 of the best analogs. These perfect-cluster selection WANI forecasts have smaller bias errors and are more highly correlated with the verifying intensities at all forecast intervals through 120 h. Without further bias correction and calibration, the cluster WANI intensity spreads are under-determined as the Probability of Detections are smaller than the desired 68%. Four examples of WANI cluster predictions of intensity bifurcation situations are provided to illustrate how a correct choice of the intensity forecast and the intensity spread can be the basis for improved warnings of the threat from western North Pacific tropical cyclones.

Improvement of Medium-Range Forecasts Using the Analog-Dynamical Method

AbstractThis study further develops the analog-dynamical method and applies it to medium-range weather forecasts. By regarding the forecast field as a small disturbance superimposed on historical analog fields, historical analog errors can be used to estimate and correct forecast errors. This method is applied to 10-day forecasts from the Global and Regional Assimilation and Prediction System (GRAPES). Both the distribution of atmospheric circulation and the pattern of sea surface temperature (SST) are considered in choosing the analog samples from a historical dataset for 2001–10 based on NCEP Final (FNL) data. The results demonstrate that the analog-dynamical method greatly reduces forecast errors and extends the period of validity of the global 500-hPa height field by 0.8 days, which is superior to results obtained using systematic correction. The correction effect at 500 hPa is increasingly significant when the lead time increases. Although the analogs are selected using 500-hPa height fields, the forecast skill at all vertical levels is improved. The average increase of the anomaly correlation coefficient (ACC) is 0.07, and the root-mean-square error (RMSE) is decreased by 10 gpm on average at a lead time of 10 days. The magnitude of errors for most forecast fields, such as height, temperature, and kinetic energy is decreased considerably by inverse correction. The model improvement is primarily a result of improvement for planetary-scale waves, while the correction for synoptic-scale waves does not affect model forecast skill. As this method is easy to operate and transport to other sophisticated models, it could be appropriate for operational use.

Meteorological subdivisional-level extended range forecast over India during southwest monsoon 2012

The real-time multi-model ensemble (MME)-based extended range (up to 3 weeks) forecast of monsoon rainfall over India during the 2012 monsoon season is analyzed using the outputs of European Centre for Medium Range Weather Forecasts (ECMWF) monthly forecast coupled model, National Centre for Environmental Prediction (NCEP) Climate Forecast System version 2 coupled model and Japan Meteorological Agency (JMA)’ ensemble prediction system. Although the individual models show useful skill in predicting the extended range forecast of monsoon, the MME forecast is found to be superior compared to these. For the country as a whole, the correlation coefficient (CC) between the observed and MME forecast rainfall departure is found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days). Over the four homogeneous regions of India, the CC is found to be significant (above 95 % level) up to 2 weeks except in case of northeast India, which shows significant CC for week 1 (days 5–11) only. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the mean percentage of correct forecast is found to be much higher than the climatology forecast. Considering the complex problem of forecasting of monsoon in the extended range timescales, the MME-based predictions for 2–3 weeks provide skillful results and useful guidance for application in agriculture and other sectors in India.

Comparison of intra-seasonal forecast of Indian summer monsoon between two versions of NCEP coupled models

The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19–25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5–11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8–10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.

Forecast scheme and strategy for extended-range predictable components

Although extended-range forecasting has exceeded the limit of daily predictability of weather, there are still partially predictable characteristics of meteorological fields in such forecasts. A targeted forecast scheme and strategy for extended-range predictable components is proposed. Based on chaotic characteristics of the atmosphere, predictable components and unpredictable random components are separated by using the standpoint of error growth in a numerical model. The predictable components are defined as those with slow error growth at a given range, which are not sensitive to small errors in initial conditions. A numerical model for predictable components (NMPC) is established, by filtering random components with poor predictability. The aim is to maintain predictable components and avoid the influence of rapidly growing forecast errors on small scales. Meanwhile, the analogue-dynamical approach (ADA) is used to correct forecast errors of predictable components, to decrease model error and statistically take into account the influence of random components. The scheme is applied to operational dynamical extended-range forecast (DERF) model of the National Climate Center of China Meteorological Administration (NCC/CMA). Prediction results show that the scheme can improve forecast skill of predictable components to some extent, especially in high predictability regions. Forecast skill at zonal wave zero is improved more than for ultra-long waves and synoptic-scale waves. Results show good agreement with predictability of spatial scale. As a result, the scheme can reduce forecast errors and improve forecast skill, which favors operational use.

Dynamic strategies to optimize asset allocation: empirical evidence in the Brazilian market

Short-term return bear influence on common investors and fund managers. However, the correct forecast of short-term market movements is not a trivial task. The purpose of this essay is to verify, according to Herold et al. (2007), if the dynamic allocation amongst main Brazilian asset classes can generate long-term gains and limit losses in shorter periods. The test results involving Ibovespa as the only risk asset confirmed this purpose. Tests involving fixed-income assets, variable-income assets and inflation-linked assets proved that the return is limited by this strategy. Static allocation and protection strategies were concurrently tested for short-term situations.

ENSURF: multi-model sea level forecast – implementation and validation results for the IBIROOS and Western Mediterranean regions

Abstract. ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast that makes use of several storm surge or circulation models and near-real time tide gauge data in the region, with the following main goals: 1. providing easy access to existing forecasts, as well as to its performance and model validation, by means of an adequate visualization tool; 2. generation of better forecasts of sea level, including confidence intervals, by means of the Bayesian Model Average technique (BMA). The Bayesian Model Average technique generates an overall forecast probability density function (PDF) by making a weighted average of the individual forecasts PDF's; the weights represent the Bayesian likelihood that a model will give the correct forecast and are continuously updated based on the performance of the models during a recent training period. This implies the technique needs the availability of sea level data from tide gauges in near-real time. The system was implemented for the European Atlantic facade (IBIROOS region) and Western Mediterranean coast based on the MATROOS visualization tool developed by Deltares. Results of validation of the different models and BMA implementation for the main harbours are presented for these regions where this kind of activity is performed for the first time. The system is currently operational at Puertos del Estado and has proved to be useful in the detection of calibration problems in some of the circulation models, in the identification of the systematic differences between baroclinic and barotropic models for sea level forecasts and to demonstrate the feasibility of providing an overall probabilistic forecast, based on the BMA method.

The real estate market as the foundation of the value and forecast as a purpose: trend and real estate cycles

The purpose of this project is that of inquiring around the relations existing among the main variables characterizing the Genovese residential real estate market which determine their performance in time through the data analysis of the two observers of the real estate market: ASSIMIL and OMI. The analysis has developed on five selected market homogeneous areas within the territory of the town of Genoa. The aim is that to test whether it’s possible or not to recognize a cyclical of values in time and how they appear by using two different sources of data. The correct forecast of the real estate market is, in fact, strategic for the valuation of the feasibility of interventions and for the construction of reliable real estate investment scenarios.

Increase in Probability of the Tsunami Formation Correct Forecast

The presented paper exhibits theory of the “gravitational” wave propagation near the Earth surface and in the ocean. There was determined an expression “gravitational” wave which was registered by the gravimeters being placed in several points of the Earth globe. Alteration of gravitational field was accompanied by alteration of the “gravitational” wave which has the velocity differing from the velocity of seismic waves. The theoretical model was proved by many experiments realized under registration of the underwater earthquake core by tens of gravimeters being placed in the Earth globe different points. The “gravitational” waves assist to increase the right forecast probability of the beginning tsunami to 50%.

A study on terrain-induced tropical cyclone looping in East Taiwan: case study of Typhoon Haitang in 2005

A mesoscale mountain such as the Central Mountain Range (CMR) of Taiwan can considerably affect the motion and the structure of an approaching tropical cyclone in very complicated ways. Many studies have presented and explained the mechanism for the change of moving speed, track deflection, or track discontinuity. In the past, the tracks of Typhoon Mary (1965), Tropical Cyclone Polly (1993), and Typhoon Haitang (2005) have a looping motion prior to making landfall on Taiwan. When the looping occurs, the timing and the intensity of the typhoon to affect Taiwan can be very different from the one moving with smooth track. It is very important to have a correct forecast for disaster mitigation operations. However, the mechanism causing such looping has not been fully understood. In this study, the case of Typhoon Haitang is examined. The radar reflectivity diagrams are used to illustrate looping in its track. By applying piecewise potential vorticity inversion analysis and numerical model simulations, it is shown that the looping motion of Typhoon Haitang near Taiwan resulted from the CMR terrain effect. When Typhoon Haitang approaches Taiwan and its circulation encounters the CMR, the terrain induces a cyclonic vortex in the lee side of the mountain over southeast of Taiwan. This terrain-induced vortex interacts with Typhoon Haitang in a way similar to the binary vortex interaction. The two vortex centers rotate around their system center, which causes looping of Typhoon Haitang.

Application of six-sigma to construct forecasting model — an example of fast-food chains in Taiwan

It is a trend that people eating out more and more often from a few decades ago. So the food and beverage industry prospered from then on. However at this time, the deflation caused by financial tsunami causes the dining price is not only fails to rise, sometimes the restaurants have to make price promotions for more sales. To raise profits, apart from selling, it is necessary to reduce waste and lower costs. But the ingredients of restaurants are perishable, it is easy to exceed the retention period and must be scrapped. Additionally, food diversification requirements of customers also leads to higher cost of shortage or excess of food prepared. The source of the cost problem is due to the errors of sales forecasting. There are two kinds of errors of sales forecasting that lead to the cost problem. One is cost of scraps due to over sales forecasting, the other is cost of shortage due to under sales forecasting. So the precision of forecasting is very important to the profitability and market share whether in the manufacturing or in service industry. However, the literatures related to the forecast are stressed on the forecast accurate without taking into account the lost cost of type I and type II errors caused by the forecasting. In this paper, authors apply the DMAIC process with performance evaluation matrix to the fast-food restaurant in Taiwan as the research object, to build a control chart of loss cost, and multiple regression model to correct forecast mode, in order to reduce the type I and type II error costs of losses, thereby enhance the competitiveness of enterprises in order to create profitability and market growth rate.

How reliable are forecasts of GDP growth and inflation for countries with limited coverage?

This paper analyses the performance of GDP growth and inflation forecasts for 25 transition countries between 1994 and 2007, as provided by 13 international institutions, including multilateral, private and academic forecasters. The empirical results show that there is a positive correlation between the number of forecasters covering a given country and the forecast accuracy. Simple combined forecasts are shown to be unbiased and more accurate than most of the individual forecasters, although also inefficient. However, only a few institutions provide efficient and unbiased forecasts, with just one out of 13 forecasters providing both unbiased and efficient forecasts of both GDP growth and inflation in the observed period. The directional analysis shows a correct forecast of the change in the forecast indicator in over two thirds of cases. However, the eventual outcome is within the range of available forecasts in less than half of the cases, with more than 40% of outcomes for GDP growth above the highest forecast. Encouragingly, forecasts are shown to be improving over time and becoming more accurate with the increase in the number of forecasting institutions – forecast accuracy measured by mean absolute error improves by 0.3 percentage points for growth and by 0.2 percentage points for inflation for each additional institution providing forecasts.

INVESTIGATION ON REAL-TIME FORECASTING SYSTEM OF STORM SURGE AT THE NORTHERN TOKUSHIMA COAST

We investigated the real-time forecast system of a storm surge to reduce its disaster. Typhoon properties such as central atmospheric pressure were forecasted before several hours by two methods. The one is forecasting by using neural network technique based on the past typhoon data and the other is utilization of typhoon forecast information by the Meteorological Agency. As a result of the comparison of both methods, it was found that the latter gave more correct forecast of typhoon properties. Based on the forecasting values of typhoon, we carried out the real-time forecasting of a storm surge and examined the practical utility of this system.