Performance evaluation of weights selection schemes for linear combination of multiple forecasts

Adhikari, Ratnadip; Agrawal, R. K.

doi:10.1007/s10462-012-9361-z

Cited by 52 publications

(48 citation statements)

References 28 publications

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“…The Monte Carlo methods include all proceedings aimed at finding approximate solutions of some problems (mathematical, technical or operational) [27]. The Monte Carlo method involves estimating the probability of occurrence of certain events based on previous studies [1,12]. The assessment of the polyethylene pipes properties of gas networks in terms of operation safety, as well as activities that influence the increase of operation reliability and safety improvement was proposed in [3].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the gas network failure and failure prediction using the Monte Carlo simulation method

Tchórzewska-Cieślak

Pietrucha-Urbanik

Urbanik

2016

EiN

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Article citation info: IntroductionIn the report of the Committee of Union Gas in the World Congress in Nice in 1972, the issue connected with the failure intensity of the gas network was presented. Operation of Gas Supply System is inseparably linked with the occurrence of the fire and explosion risk. Failures, explosions and fires long-term statistics conducted by the gas services and organizational units of the State Fire Service indicate that despite the continuous increase of the safety level of gas supply system and number of actions taken in this regard, still many failures are recorded, which often generate serious threat for the external and the inner environment of a man, as well as create risk for human health and life. The consequences of failures, during which the release and dispersion of natural gas occurs, depend on its type, nature and causes, as well as from the efficiency of its removal.Failure frequency evaluation in the operation of gas supply subsystem should be one of the priorities of the gasworks, which should attach greater importance to assess the proper functioning of the newly designed, as well as the implemented systems [31]. With the increasing pressure from the environmentalists and stricter standards for acceptable environmental pollution and structures vulnerability in terms of its protection, it seems to be a necessity [6,24,28]. The issue connected with failure gas pipeline analysis in perspective of improper design, construction and maintenance is presented in work [11], the increased failure affected by the lack or improper conduct of repairs and modernization, also incorrect or lack of risk management program. When planning renovations or modernization of gas networks the gasworks employees should make the classification of pipelines, that means determine which sections of the network require immediate repair, and which can be repaired later [7,15,26]. For this reason, making the appropriate maintenance (repair, renovation, replacement) only after the damage of the element may be irrational, control human intervention detects and removes faults being a potential source of failure. Widely used solution become the preventive renewals, aimed at reducing the loss of utility of a given element in different environment [4,8,21]. Very important from operator perspective is the failure reason [18,20,29], as well as modernizing actions that should be taken to avoid such undesirable situations. The strategy of the preventive renewals is to establish such timing of the renewals which will enable to achieve the maximum profitability of the project, through the use of the periodic strategies involving the renewals after a certain period of element operation [5,11]. Such classification can be made, for example, on the basis of failure prediction for certain sections of the gas network, using the Monte Carlo method [30,34]. The Monte Carlo methods include all proceedings aimed at finding approximate solutions of some problems (mathematical, technical or operational) [27]. The Monte Carlo ...

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Section: Introductionmentioning

confidence: 99%

Analysis of the gas network failure and failure prediction using the Monte Carlo simulation method

Tchórzewska-Cieślak

Pietrucha-Urbanik

Urbanik

2016

EiN

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“…Assuming that a total of LSTM models in an ensemble are provided, their ensemble forecast result for time series, denoted as ( (1) , (2) . .…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…To this end, forecast (prediction) models are needed to predict the future based on historical data [1]. The traditional mathematical (statistical) models, such as Least Square Regression (LSR) [2], Autoregressive Moving Average [3][4][5], and Neural Networks [6], were widely used and reported in literature for their utility in practical time series forecasting.…”

Section: Introductionmentioning

confidence: 99%

“…To cope with this limitation, weighted combination schemes have been proposed. The authors in [2] proposed the Least Square Regression (LSR) that attempts to find the optimal weights by minimizing the Sum of Squared Error (SSE). The authors in [15] adopted the Average of In-sample Weights (AIW) scheme where each weight is simply computed as the normalized inverse absolute forecasting error of an individual model [16].…”

Section: Introductionmentioning

confidence: 99%

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Combining LSTM Network Ensemble via Adaptive Weighting for Improved Time Series Forecasting

Choi

Lee

2018

Mathematical Problems in Engineering

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Time series forecasting is essential for various engineering applications in finance, geology, and information technology, etc. Long Short-Term Memory (LSTM) networks are nowadays gaining renewed interest and they are replacing many practical implementations of the time series forecasting systems. This paper presents a novel LSTM ensemble forecasting algorithm that effectively combines multiple forecast (prediction) results from a set of individual LSTM networks. The main advantages of our LSTM ensemble method over other state-of-the-art ensemble techniques are summarized as follows: (1) we develop a novel way of dynamically adjusting the combining weights that are used for combining multiple LSTM models to produce the composite prediction output; for this, our method is devised for updating combining weights at each time step in an adaptive and recursive way by using both past prediction errors and forgetting weight factor; (2) our method is capable of well capturing nonlinear statistical properties in the time series, which considerably improves the forecasting accuracy; (3) our method is straightforward to implement and computationally efficient when it comes to runtime performance because it does not require the complex optimization in the process of finding combining weights. Comparative experiments demonstrate that our proposed LSTM ensemble method achieves state-of-the-art forecasting performance on four real-life time series datasets publicly available.

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“…Kalman filters [10] or Artificial Neural Networks [11]). A collective consideration of several forecast models has proven to be a more powerful approach than just relying on one individual technique [12]. Consequently, we follow the idea of ensemble forecasting, combining the forecast of several independent techniques.…”

Section: Short-term Traffic Forecastingmentioning

confidence: 99%

Anticipatory Adaptation of Signalisation Based on Traffic Flow Forecasts within a Self-organised Traffic Control System

Sommer

Hähner

2016

MATEC Web Conf.

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Abstract. Autonomously adapting signalling strategies to changing traffic demand in urban areas have been frequently used as application scenario for self-adapting systems. Striving for the ability to cope with the dynamic behaviour of traffic and to react appropriately to unforeseen conditions, such solutions dynamically adapt the signalisation to the monitored traffic demands. The Organic Traffic Control (OTC) system is one of the most prominent representatives in this domain. OTC implements a multi-layered observer-/controller architecture. In this paper, we extend OTC's observer with a time series forecast component to create forecasts of future traffic developments for turning movements. These forecasts are then used to proactively adapt signalisation parameters. We demonstrate the benefit of the developed approach in terms of reduced travel times, and vehicle emissions within near-to-reality simulations of realistic traffic conditions from Hamburg, Germany.

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Performance evaluation of weights selection schemes for linear combination of multiple forecasts

Cited by 52 publications

References 28 publications

Analysis of the gas network failure and failure prediction using the Monte Carlo simulation method

Analysis of the gas network failure and failure prediction using the Monte Carlo simulation method

Combining LSTM Network Ensemble via Adaptive Weighting for Improved Time Series Forecasting

Anticipatory Adaptation of Signalisation Based on Traffic Flow Forecasts within a Self-organised Traffic Control System

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