Determining The Optimal Values Of Exponential Smoothing Constants Does Solver Really Work?

Ravinder, Handanhal

doi:10.19030/ajbe.v6i3.7815

Cited by 16 publications

(10 citation statements)

References 6 publications

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“…We used the generalized reduced gradient as the optimization algorithm [134], which searches for the extreme values of the functions by the generalized reduced gradient algorithm method (GRG2) [135][136][137][138]. We used the multi-start method to find a globally optimal solution [139,140]. The multi-start method operates by generating candidate starting point values randomly selected between the bounds specified for the variables.…”

Section: Goodness-of-fit Testsmentioning

confidence: 99%

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Evaluating Hydrological Models for Deriving Water Resources in Peninsular Spain

et al. 2019

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Water availability is essential for the appropriate analysis of its sustainable management. We performed a comparative study of six hydrological balance models (Témez, ABCD, GR2M, AWBM, GUO-5p, and Thornthwaite-Mather) in several basins with different climatic conditions within Spain in the 1977–2010 period. We applied six statistical indices to compare the results of the models: the Akaike information criterion (AIC), the Bayesian information criterion (BIC), Nash–Sutcliffe model efficiency coefficient (NSE), coefficient of determination (R2), percent bias (PBIAS), and the relative error between observed and simulated run-off volumes (REV). Furthermore, we applied the FITEVAL software to determine the uncertainty of the model. The results show that when the catchments are more humid the obtained results are better. The GR2M model gave the best fit in peninsular Spain in a UNEP aridity index framework above 1, and NSE values above 0.75 in a 95% confidence interval classify GR2M as very good for humid watersheds. The use of REV is also a key index in the assessment of the margin of error. Flow duration curves show good performance in the probabilities of exceedance lower than 80% in wet watersheds and deviations in low streamflows account for less than 5% of the total streamflow.

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Section: Goodness-of-fit Testsmentioning

confidence: 99%

“…As the number of runs of the non-linear iterations increases, the probability that the globally optimal solution has been found also increases 100%. For most non-linear problems, this method will at least yield very good solutions [140]. In this study, 1000 starting points have been used for each model and watershed.…”

Section: Goodness-of-fit Testsmentioning

confidence: 99%

Evaluating Hydrological Models for Deriving Water Resources in Peninsular Spain

et al. 2019

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“…With spatial and temporal variability of floating cars in the overall traffic, the traditional single exponential smoothing algorithm is modified so that a suitable forecast can be produced in case of missing data in a time interval. The value of smoothing constant (α) is selected as 0.2 suggested by [23] and [24]. The proposed algorithm (see Fig.…”

Section: A Link-level Speed Forecastingmentioning

confidence: 99%

Controller Independent Software-in-the-Loop Approach to Evaluate Rule-Based Traffic Signal Retiming Strategy by Utilizing Floating Car Data

Sharma

Lubmann

2019

IEEE Trans. Intell. Transport. Syst.

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Floating car data present a cost-effective approach to observing the traffic state. This paper explores whether floating cars can substitute stationary detection devices (e.g., induction loops) for observers within traffic responsive control systems. A rule-based traffic control method at the local intersection level is proposed in this paper by utilizing the floating car data. The control method involves a threefold approach: link-level speed forecasting, data-driven traffic flow estimation, and split optimization. To estimate traffic flow, a multivariable linear regression model is developed by utilizing forecasted link-level speed, signal control variables, and link length as predictors. The method is tested using a controller (hardware)independent software-in-the-loop approach. Compared with the existing fixed-time control operating in Starnberg, Germany, the proposed method is able to improve the level of service of the signalized intersection when tested for different levels of market penetration of the floating cars. The findings underpin the use of floating car data in online traffic control applications; the benefits will increase with an increase in market penetration of floating cars. Overall, this paper presents a fully integrated technical system that is ready to be used in the field. The proposed system can be implemented at the tactical level of urban traffic-control hierarchy employed in Germany.

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“…In some of the works, authors used classical non-linear optimization methods with constrained values of variables, to optimize HW parameters. In [47] authors used the MS Excel Nonlinear Solver, a spreadsheet-based non-linear optimizer, to find the values of the smoothing parameters, together with an initial forecast that minimize a measure of forecast error MAD or MSE. A detailed description is given to avoid problems reported by several other authors.…”

Section: Review Of Predictive and Not Predictive Algorithms For Real-mentioning

confidence: 99%

Survey and Proposal of an Adaptive Anomaly Detection Algorithm for Periodic Data Streams

Hasani¹,

Krrabaj²

2019

JCC

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Real-time anomaly detection of massive data streams is an important research topic nowadays due to the fact that a lot of data is generated in continuous temporal processes. There is a broad research area, covering mathematical, statistical, information theory methodologies for anomaly detection. It addresses various problems in a lot of domains such as health, education, finance, government, etc. In this paper, we analyze the state-of-the-art of data streams anomaly detection techniques and algorithms for anomaly detection in data streams (time series data). Critically surveying the techniques' performances under the challenge of real-time anomaly detection of massive high-velocity streams, we conclude that the modeling of the normal behavior of the stream is a suitable approach. We evaluate Holt-Winters (HW), Taylor's Double Holt-Winters (TDHW), Hierarchical temporal memory (HTM), Moving Average (MA), Autoregressive integrated moving average (ARIMA) forecasting models, etc. Holt-Winters (HW) and Taylor's Double Holt-Winters (TDHW) forecasting models are used to predict the normal behavior of the periodic streams, and to detect anomalies when the deviations of observed and predicted values exceeded some predefined measures. In this work, we propose an enhancement of this approach and give a short description about the algorithms and then they are categorized by type of prediction as: predictive and non-predictive algorithms. We implement the Genetic Algorithm (GA) to periodically optimize HW and TDHW smoothing parameters in addition to the two sliding windows parameters that improve Hyndman's MASE measure of deviation, and value of the threshold parameter that defines no anomaly confidence interval [1]. We also propose a new optimization function based on the input training datasets with the annotated anomaly intervals, in order to detect the right anomalies and minimize the number of false ones. The proposed method is evaluated on the known anomaly detection benchmarks NUMENTA and Yahoo datasets with annotated anomalies and real log data generated by the National education information system

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Determining The Optimal Values Of Exponential Smoothing Constants Does Solver Really Work?

Cited by 16 publications

References 6 publications

Evaluating Hydrological Models for Deriving Water Resources in Peninsular Spain

Evaluating Hydrological Models for Deriving Water Resources in Peninsular Spain

Controller Independent Software-in-the-Loop Approach to Evaluate Rule-Based Traffic Signal Retiming Strategy by Utilizing Floating Car Data

Survey and Proposal of an Adaptive Anomaly Detection Algorithm for Periodic Data Streams

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