Browsing by Subject "Forecasting"

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Open Access
3-Boyutlu orman yangını yayılımı sistemi
(IEEE, 2008) Köse, Kıvanç; Yılmaz, E.; Grammalidis, N.; Aktuğ, B.; Çetin, A. Enis; Aydın, İ.
In the last few years, due to the global warming and draught related to it, there is an increase in the number of forest fires. Forest fire detection is mainly done by people but there exists some automated systems in this field too. Besides the detection of the forest fires, effective fire extinhguising has an important role in fire fighting. If the spread of the fire can be predicted from the starting, early intervene can be achieved and fire can be extinguished swiftly. Using the Fire Propagation Simulator explained here it is aimed, to predict the fire development beforehand and to visulalize this predictions on a 3D-GIS environment. ©2008 IEEE.
Open Access
Adaptive compute-phase prediction and thread prioritization to mitigate memory access latency
(ACM, 2014-06) Aktürk, İsmail; Öztürk, Özcan
The full potential of chip multiprocessors remains unex- ploited due to the thread oblivious memory access sched- ulers used in off-chip main memory controllers. This is especially pronounced in embedded systems due to limita- Tions in memory. We propose an adaptive compute-phase prediction and thread prioritization algorithm for memory access scheduling for embedded chip multiprocessors. The proposed algorithm eficiently categorize threads based on execution characteristics and provides fine-grained priori- Tization that allows to differentiate threads and prioritize their memory access requests accordingly. The threads in compute phase are prioritized among the threads in mem- ory phase. Furthermore, the threads in compute phase are prioritized among themselves based on the potential of mak- ing more progress in their execution. Compared to the prior works First-Ready First-Come First-Serve (FR-FCFS) and Compute-phase Prediction with Writeback-Refresh Overlap (CP-WO), the proposed algorithm reduces the execution time of the generated workloads up to 23.6% and 12.9%, respectively. Copyright 2014 ACM.
Open Access
Analysis of errors in zero-free-parameter modeling approach to predict the voltage of electrochemical energy storage systems under arbitrary load
(Electrochemical Society, 2017) Ulgut, Burak; Uzundal, Can Berk; Özdemir, Elif
In a recently published article (J. Electrochem. Soc. 164 (2017) A1274-A1280), we described a new method to predict the voltage response of electrochemical energy storage systems during arbitrary load profiles. Our work shows that the impedance spectrum can be employed in the frequency domain in order to ultimately calculate the time domain behavior of the electrochemical energy storage system. The big advantage of this method is the fact that there are no free parameters and fits throughout. The present work deals with the sources of error in the above-mentioned prediction approach and looks for the effects of the various sources of error. The current analysis concludes that two big contributors to the overall error are the inaccuracies in the DC part of the prediction and the non-linearities that are not modeled by a linear impedance spectrum. Discussions are also made regarding ways to improve the performance of the modeling approach the most and where future work is going to be looking to improve.
Open Access
An Application of Expected Utility Modeling and Game Theory in IR: Assessment of International Bargaining on Iran’s Nuclear Program
(Dış Politika ve Barış Araştırmaları Merkezi, İhsan Doğramacı Barış Vakfı, 2019) Özdamar, Özgür
This article provides an introduction to the theoretical underpinnings of expected utility and game theory approaches in IR studies. It goes on to explore their application to a specific research subject, international bargaining on Iran’s nuclear program. In this application, the article presents forecasts about Iran’s nuclear program using a game theoretic, bounded rationality model called the expected utility model (Bueno de Mesquita 2002). Three analyses were made in December 2005, September 2006 and March 2007. All three forecasts appear to be in line with real-life developments regarding the issue. The results show that Iran has been losing international support since the analyses started, and the last forecast suggests a pro-US position supported by all major international actors. Also, all three analyses suggest that Russian and Chinese support is vital to curb the Iranian nuclear program.
Open Access
Application of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillation
(Springer, 2015) Ravens, U.; Katircioglu-Öztürk, D.; Wettwer, E.; Christ, T.; Dobrev, D.; Voigt, N.; Poulet, C.; Loose, S.; Simon, J.; Stein, A.; Matschke, K.; Knaut, M.; Oto, E.; Oto, A.; Güvenir, H. A.
Ex vivo recorded action potentials (APs) in human right atrial tissue from patients in sinus rhythm (SR) or atrial fibrillation (AF) display a characteristic spike-and-dome or triangular shape, respectively, but variability is huge within each rhythm group. The aim of our study was to apply the machine-learning algorithm ranking instances by maximizing the area under the ROC curve (RIMARC) to a large data set of 480 APs combined with retrospectively collected general clinical parameters and to test whether the rules learned by the RIMARC algorithm can be used for accurately classifying the preoperative rhythm status. APs were included from 221 SR and 158 AF patients. During a learning phase, the RIMARC algorithm established a ranking order of 62 features by predictive value for SR or AF. The model was then challenged with an additional test set of features from 28 patients in whom rhythm status was blinded. The accuracy of the risk prediction for AF by the model was very good (0.93) when all features were used. Without the seven AP features, accuracy still reached 0.71. In conclusion, we have shown that training the machine-learning algorithm RIMARC with an experimental and clinical data set allows predicting a classification in a test data set with high accuracy. In a clinical setting, this approach may prove useful for finding hypothesis-generating associations between different parameters.
Open Access
Benefits of forecasting and energy storage in isolated grids with large wind penetration – The case of Sao Vicente
(Elsevier, 2017) Yuan, S.; Kocaman, A.S.; Modi, V.
For electric grids that rely primarily on liquid fuel based power generation for energy provision, e.g. one or more diesel gensets, measures to allow a larger fraction of intermittent sources can pay-off since the displaced is high cost diesel powered generation. This paper presents a case study of Sao Vicente, located in Cape Verde where a particularly high fraction of wind capacity of 5.950ï¿½MW (75% of the average demand) is installed, with diesel gensets forming the dispatchable source of power. This high penetration of intermittent power is managed through conservative forecasting and curtailments. Two potential approaches to reduce curtailments are examined in this paper: 1) an improved wind speed forecasting using a rolling horizon ARIMA model; and 2) energy storage. This case study shows that combining renewable energy forecasting and energy storage is a promising solution which enhances diesel fuel savings as well as enables the isolated grid to further increase the annual renewable energy penetration from the current 30.4% up to 38% while reducing grid unreliability. In general, since renewable energy forecasting ensures more accurate scheduling and energy storage absorbs scheduling error, this solution is applicable to any small size isolated power grid with large renewable energy penetration.
Open Access
Çağrı merkezi metin madenciliği yaklaşımı
(IEEE, 2017-05) Yiğit, İ. O.; Ateş, A. F.; Güvercin, Mehmet; Ferhatosmanoğlu, Hakan; Gedik, Buğra
Günümüzde çağrı merkezlerindeki görüşme kayıtlarının sesten metne dönüştürülebilmesi görüşme kaydı metinleri üzerinde metin madenciliği yöntemlerinin uygulanmasını mümkün kılmaktadır. Bu çalışma kapsamında görüşme kaydı metinleri kullanarak görüşmenin içeriğinin duygu yönünden (olumlu/olumsuz) değerlendirilmesi, müşteri memnuniyetinin ve müşteri temsilcisi performansının ölçülmesi amaçlanmaktadır. Yapılan çalışmada görüşme kaydı metinlerinden metin madenciliği yöntemleri ile yeni özellikler çıkarılmıştır. Metinlerden elde edilen özelliklerden yararlanılarak sınıflandırma ve regresyon yöntemleriyle görüşme kayıtlarının içeriklerinin değerlendirilmesini sağlayacak tahmin modelleri oluşturulmuştur. Bu çalışma sonucunda ortaya çıkarılan tahmin modellerinin Türk Telekom bünyesindeki çağrı merkezlerinde kullanılması hedeflenmektedir.
Embargo
Can investors’ informed trading predict cryptocurrency returns? Evidence from machine learning
(Elsevier Inc., 2022-05-24) Wang, Y.; Wang, C.; Şensoy, Ahmet; Yao, S.; Cheng, F.
As an emerging asset, cryptocurrencies have attracted more and more attention from investors and researchers in recent years. With the gradual convergence of the investors in cryptocurrency and traditional financial markets, the research on investor trading behavior from the perspective of microstructure has become increasingly important in cryptocurrency market. In this paper, we study whether investors’ informed trading behavior can significantly predict cryptocurrency returns. We use various machine learning algorithms to verify the contribution of informed trading to the predictability of cryptocurrency returns. The results show that informed trading plays a role in the prediction of some individual cryptocurrency returns, but it cannot significantly improve the prediction accuracy in an average sense of the whole market. The lack of market supervision of cryptocurrency market may be the main factor for relatively low efficiency of this market, and policymakers need to pay attention to it.
Open Access
Comprehensive lower bounds on sequential prediction
(IEEE, 2014-09) Vanlı, N. Denizcan; Sayın, Muhammed O.; Ergüt, S.; Kozat, Süleyman S.
We study the problem of sequential prediction of real-valued sequences under the squared error loss function. While refraining from any statistical and structural assumptions on the underlying sequence, we introduce a competitive approach to this problem and compare the performance of a sequential algorithm with respect to the large and continuous class of parametric predictors. We define the performance difference between a sequential algorithm and the best parametric predictor as regret, and introduce a guaranteed worst-case lower bounds to this relative performance measure. In particular, we prove that for any sequential algorithm, there always exists a sequence for which this regret is lower bounded by zero. We then extend this result by showing that the prediction problem can be transformed into a parameter estimation problem if the class of parametric predictors satisfy a certain property, and provide a comprehensive lower bound to this case.
Open Access
Computer intensive techniques for model selection
(1998) Başçı, Sıdıka
There are three essays in this dissertation. In the first one, which appears in Chapter 2, a comparison of finite sample performances of six model selection criteria for Autoregressive (AR) processes exists. Simulation results report the effects of being parsimonious while selecting the model on forecasting. Moreover, in the chapter the assumption of normality, which can be seen in all of the previous theoretical and emprical studies, is relaxed and performances of the criteria under non-normal distributions are investigated. The second essay is presented in Chapter 3. In this essay three new model selection criteria are suggested where cross-validated estimates of variances are used. In the chapter, a comparison of the finite sample performances of these new criteria with the already existing ones is presented. The main concern of the third essay, that appears in Chapter 4, is detecting structural change when the change point is unknown. In the chapter, we derive some Bayesian tests to detect structural change with unknown change point under the assumptions of different prior distributions.
Open Access
Cross-term free based bistatic radar system using sparse least squares
(SPIE, 2015) Sevimli, R. Akın; Çetin, A. Enis
Passive Bistatic Radar (PBR) systems use illuminators of opportunity, such as FM, TV, and DAB broadcasts. The most common illuminator of opportunity used in PBR systems is the FM radio stations. Single FM channel based PBR systems do not have high range resolution and may turn out to be noisy. In order to enhance the range resolution of the PBR systems algorithms using several FM channels at the same time are proposed. In standard methods, consecutive FM channels are translated to baseband as is and fed to the matched filter to compute the range-Doppler map. Multichannel FM based PBR systems have better range resolution than single channel systems. However superious sidelobe peaks occur as a side effect. In this article, we linearly predict the surveillance signal using the modulated and delayed reference signal components. We vary the modulation frequency and the delay to cover the entire range-Doppler plane. Whenever there is a target at a specific range value and Doppler value the prediction error is minimized. The cost function of the linear prediction equation has three components. The first term is the real-part of the ordinary least squares term, the second-Term is the imaginary part of the least squares and the third component is the l2-norm of the prediction coefficients. Separate minimization of real and imaginary parts reduces the side lobes and decrease the noise level of the range-Doppler map. The third term enforces the sparse solution on the least squares problem. We experimentally observed that this approach is better than both the standard least squares and other sparse least squares approaches in terms of side lobes. Extensive simulation examples will be presented in the final form of the paper.
Open Access
Currency forecasting: an investigation of extrapolative judgement
(Elsevier, 1997) Wilkie-Thomson, M. E.; Önkal-Atay, D.; Pollock, A. C.
This paper aims to explore the potential effects of trend type, noise and forecast horizon on experts' and novices' probabilistic forecasts. The subjects made forecasts over six time horizons from simulated monthly currency series based on a random walk, with zero, constant and stochastic drift, at two noise levels. The difference between the Mean Absolute Probability Score of each participant and an AR(1) model was used to evaluate performance. The results showed that the experts performed better than the novices, although worse than the model except in the case of zero drift series. No clear expertise effects occurred over horizons, albeit subjects' performance relative to the model improved as the horizon increased. Possible explanations are offered and some suggestions for future research are outlined.
Open Access
Do DSGE models forecast more accurately out-of sample than VAR models?
(Emerald, 2013) Gürkaynak, Refet S.; Kisacikoǧlu, B.; Rossi, B.
Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately. Copyright © 2013 by Emerald Group Publishing Limited.
Open Access
An eager regression method based on best feature projections
(Springer, Berlin, Heidelberg, 2001) Aydın, Tolga; Güvenir, H. Altay
This paper describes a machine learning method, called Regression by Selecting Best Feature Projections (RSBFP). In the training phase, RSBFP projects the training data on each feature dimension and aims to find the predictive power of each feature attribute by constructing simple linear regression lines, one per each continuous feature and number of categories per each categorical feature. Because, although the predictive power of a continuous feature is constant, it varies for each distinct value of categorical features. Then the simple linear regression lines are sorted according to their predictive power. In the querying phase of learning, the best linear regression line and thus the best feature projection are selected to make predictions. © Springer-Verlag Berlin Heidelberg 2001.
Open Access
Energy consumption forecasting via order preserving pattern matching
(IEEE, 2014-12) Vanlı, N. Denizcan; Sayın, Muhammed O.; Yıldız, Hikmet; Göze, Tolga; Kozat, Süleyman S.
We study sequential prediction of energy consumption of actual users under a generic loss/utility function. Particularly, we try to determine whether the energy usage of the consumer will increase or decrease in the future, which can be subsequently used to optimize energy consumption. To this end, we use the energy consumption history of the users and define finite state (FS) predictors according to the relative ordering patterns of these past observations. In order to alleviate the overfitting problems, we generate equivalence classes by tying several states in a nested manner. Using the resulting equivalence classes, we obtain a doubly exponential number of different FS predictors, one among which achieves the smallest accumulated loss, hence is optimal for the prediction task. We then introduce an algorithm to achieve the performance of this FS predictor among all doubly exponential number of FS predictors with a significantly reduced computational complexity. Our approach is generic in the sense that different tying configurations and loss functions can be incorporated into our framework in a straightforward manner. We illustrate the merits of the proposed algorithm using the real life energy usage data. © 2014 IEEE.
Open Access
Estimation and forecasting of PM10 air pollution in Ankara via time series and harmonic regressions
(Springer, 2020) Akdi, Y.; Okkaoğlu, Y.; Gölveren, E.; Yücel, M. Eray
In this study, monthly particulate matter (PM10) values in Ankara (39.9334° N, 32.8597° E) from January 1993 to December 2017 are examined. The PM10 are those thoracic particles whose aerodynamic diameter is less than 10 μm (micrometers), and it is of critical health importance due to the penetrability to the lower airways. As an alternative to classical unit root tests, a unit root test primarily based on periodograms is introduced owing to its advantages over alternatives. After examining the stationarity of the series through periodogram-based test as well as its standard rivals, periodic components in the series are examined and it is observed that the series has both periodic and seasonal components. These components are modeled, using the inherent dynamics of a time series alone, within a trigonometric harmonic regression setup, eventually yielding the forecast values for 2018 that turns out to be superior to those obtained by means of ARIMA (autoregressive integrated moving average). This is a striking result since the modeling framework requires no assumptions, no parameter estimations except for the variance of the white noise series, no simulations of the power of tests, no adjustments of test statistics with respect to sample size and no preliminary work as to independent variable which is simply time, i.e., the period of forecast.
Open Access
Evaluating expert advice in forecasting: Users’ reactions to presumed vs. experienced credibility
(Elsevier B.V., 2017) Önkal, Dilek; Gönül, Mustafa Sinan; Goodwin, Paul; Thomson, Mary; Öz, Esra
In expert knowledge elicitation (EKE) for forecasting, the perceived credibility of an expert is likely to affect the weighting attached to their advice. Four experiments have investigated the extent to which the implicit weighting depends on the advisor's experienced (reflecting the accuracy of their past forecasts), or presumed (based on their status) credibility. Compared to a control group, advice from a source with a high experienced credibility received a greater weighting, but having a low level of experienced credibility did not reduce the weighting. In contrast, a high presumed credibility did not increase the weighting relative to a control group, while a low presumed credibility decreased it. When there were opportunities for the two types of credibility to interact, a high experienced credibility tended to eclipse the presumed credibility if the advisees were non-experts. However, when the advisees were professionals, both the presumed and experienced credibility of the advisor were influential in determining the weight attached to the advice.
Open Access
Evaluating predictive performance of judgemental extrapolations from simulated currency series
(Elsevier, 1999) Pollock, A. C.; Macaulay, A.; Önkal-Atay, D.; Wilkie-Thomson, M. E.
Judgemental forecasting of exchange rates is critical for financial decision-making. Detailed investigations of the potential effects of time-series characteristics on judgemental currency forecasts demand the use of simulated series where the form of the signal and probability distribution of noise are known. The accuracy measures Mean Absolute Error (MAE) and Mean Squared Error (MSE) are frequently applied quantities in assessing judgemental predictive performance on actual exchange rate data. This paper illustrates that, in applying these measures to simulated series with Normally distributed noise, it may be desirable to use their expected values after standardising the noise variance. A method of calculating the expected values for the MAE and MSE is set out, and an application to financial experts' judgemental currency forecasts is presented.
Open Access
Evaluating probabilistic forecasts of stock prices in a developing stock market
(Elsevier, 1994) Önkal D.; Muradoğlu, G.
Recent literature on the accuracy of forecasting in financial markets reveals contradictory results. These discrepancies can be attributed to the differences in forecasting environments as well as the differences in forecaster expertise that are employed by the researchers. Since the use of point and interval predictions by themselves do not aid in explaining the various aspects of forecaster performance, probabilistic forecasting provides a better alternative that can be used to gain insight into forecasting accuracy in such settings. This study aims to test the effects of forecaster expertise and forecasting environment on forecasting accuracy. Accordingly, various aspects of forecasting performance are studied in a developing stock-market framework.
Open Access
Expectations, use and judgmental adjustment of external financial and economic forecasts: an empirical investigation
(John Wiley & Sons Ltd., 2009) Gönül, S.; Önkal D.; Goodwin, P.
A survey of 124 users of externally produced financial and economic forecasts in Turkey investigated their expectations and perceptions of forecast quality and their reasons for judgmentally adjusting forecasts. Expectations and quality perceptions mainly related to the timeliness of forecasts, the provision of a clear justifiable rationale and accuracy. Cost was less important. Forecasts were frequently adjusted when they lacked a justifiable explanation, when the user felt they could integrate their knowledge into the forecast, or where the user perceived a need to take responsibility for the forecast. Forecasts were less frequently adjusted when they came from a well-known source and were based on sound explanations and assumptions. The presence of feedback on. accuracy reduced the influence of these factors. The seniority and experience of users had little effect on their attitudes or propensity to make adjustments.