Introduction to Modern Time Series Analysis

Kirchgäßner, Gebhard; Wolters, Jürgen; Hassler, Uwe

doi:10.1007/978-3-642-33436-8

Cited by 144 publications

(120 citation statements)

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“…At the very end of stability range (22) both dispersion measures of η MSE β,r seem to fade out towards zero. This observation might not be accidental and it is probably caused by the strong impact of βe i−k y i−l in contrast to e i which results in increased estimation accuracy for both cost functions.…”

Section: Simulation Resultsmentioning

confidence: 97%

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Identification of stable elementary bilinear time-series model

Maliński

2016

Archives of Control Sciences

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The paper presents new approach to estimation of the coefficients of an elementary bilinear time series model (EB). Until now, a lot of authors have considered different identifiability conditions for EB models which implicated different identifiability ranges for the model coefficient. However, all of these ranges have a common feature namely they are significantly narrower than the stability range of the EB model. This paper proposes a simple but efficient solution which makes an estimation of the EB model coefficient possible within its entire stability range.

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Section: Simulation Resultsmentioning

confidence: 97%

“…They are extensively described in the literature [10], [22]. Effective identification algorithms for linear times-series models are well-known and frequently used in industry, economic and academic research.…”

Section: Introductionmentioning

confidence: 99%

Identification of stable elementary bilinear time-series model

Maliński

2016

Archives of Control Sciences

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“…The time horizon is a major aspect in the forecasting process, where seasonal, trend and random workloads for short-term, intermediate-term and long-term periods can be distinguished [37]. Aspects from the related concept of time series components by [61] may be used additionally in order to take adequate account to business plans and product lifecycles. Accordingly, mid-term developments with variable cycle length are specified as cyclic components whereas short-term seasonal components show regular changes resulting in a constant cycle length.…”

Section: Activities Of the Capacity Management Consumermentioning

confidence: 99%

“…Accordingly, mid-term developments with variable cycle length are specified as cyclic components whereas short-term seasonal components show regular changes resulting in a constant cycle length. Components may be integrated additively or multiplicatively [61]. Hence, the activity "Forecast Workload Evolution" produces workload profiles for each time horizon that are used in the subsequent activity for predicting service performance (cf.…”

Section: Activities Of the Capacity Management Consumermentioning

confidence: 99%

Capacity Management as a Service for Enterprise Standard Software

Müller

Bosse

Turowski

2018

J. Complex Syst. Inform. Model. Q.

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Abstract. Capacity management approaches optimize component utilization from a strong technical perspective. In fact, the quality of involved services is considered implicitly by linking it to resource capacity values. This practice hinders to evaluate design alternatives with respect to given service levels that are expressed in user-centric metrics such as the mean response time for a business transaction. We argue that utilized historical workload traces often contain a variety of performance-related information that allows for the integration of performance prediction techniques through machine learning. Since enterprise applications excessively make use of standard software that is shipped by large software vendors to a wide range of customers, standardized prediction models can be trained and provisioned as part of a capacity management service which we propose in this article. Therefore, we integrate knowledge discovery activities into well-known capacity planning steps, which we adapt to the special characteristics of enterprise applications. Using a realworld example, we demonstrate how prediction models that were trained on a large scale of monitoring data enable cost-efficient measurement-based prediction techniques to be used in early design and redesign phases of planned or running applications. Finally, based on the trained model, we demonstrate how to simulate and analyze future workload scenarios. Using a Pareto approach, we were able to identify cost-effective design alternatives for an enterprise application whose capacity is being managed.

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“…Forecasting methods for each of the models in Table 2 are succinctly described in [11] and are defined as follows:…”

Section: Generation Of Forecastsmentioning

confidence: 99%

The Application of Time Series Modelling and Monte Carlo Simulation: Forecasting Volatile Inventory Requirements

Davies¹,

Coole²,

Osipyw³

2014

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During the assembly of internal combustion engines, the specific size of crankshaft shell bearing is not known until the crankshaft is fitted to the engine block. Though the build requirements for the engine are consistent, the consumption profile of the different size shell bearings can follow a highly volatile trajectory due to minor variation in the dimensions of the crankshaft and engine block. The paper assesses the suitability of time series models including ARIMA and exponential smoothing as an appropriate method to forecast future requirements. Additionally, a Monte Carlo method is applied through building a VBA simulation tool in Microsoft Excel and comparing the output to the time series forecasts.

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Introduction to Modern Time Series Analysis

Cited by 144 publications

References 0 publications

Identification of stable elementary bilinear time-series model

Identification of stable elementary bilinear time-series model

Capacity Management as a Service for Enterprise Standard Software

The Application of Time Series Modelling and Monte Carlo Simulation: Forecasting Volatile Inventory Requirements

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