Comparative Analysis on Time Series With Included Structural Break

Andreeski, Cvetko; Hakim, Abdul Halim; Vasant, Pandian; Barsoum, Nader

doi:10.1063/1.3223933

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…Each series should be tested for possible structural breaks 1 . Figure 4 portrays the series with annual data of arrived domestic and foreign tourists between 2010 and 2017 -Republic of Serbia, from which the upward trend of the number of foreign tourists can be observed.…”

Section: Number Of Arrived Foreign Tourists 2010-2018mentioning

confidence: 99%

“…Alternatively, in paper [20], the authors used several competing models, mainly based on models for time series analysis and commenting on the results of modeling and forecasting of a series of arrivals in Australia. On the other hand, in paper [1] and [6] the main aim of research is the comparison between linear ARIMA models and non-linear models based on artificial neural networks. In paper [1] we can find an exploration of their performances for modeling time series with existing break(s).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, in paper [1] and [6] the main aim of research is the comparison between linear ARIMA models and non-linear models based on artificial neural networks. In paper [1] we can find an exploration of their performances for modeling time series with existing break(s). Nevertheless, many authors have worked on time series modeling with seasonal components by using neural networks [3] [6] [13] [19], as time series with a significant seasonal component are important in different areas of research like economy [7], climate forecast [17], biology [18], medicine [8], etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling, Forecasting and Testing Decisions for Seasonal Time Series in Tourism

Andreeski¹,

Mechkaroska²

2020

ACTA POLYTECH HUNG

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Section: Number Of Arrived Foreign Tourists 2010-2018mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling, Forecasting and Testing Decisions for Seasonal Time Series in Tourism

Andreeski¹,

Mechkaroska²

2020

ACTA POLYTECH HUNG

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“…For the modelling she uses standard ARIMA model as well as we can find at (Baldigara & Mamula, 2015). In the paper (Andreeski & Vasant, 2009) we can find the comparison between linear ARIMA models and non-linear models based on artificial neural networks. In the paper we can find exploration of their performances for modeling on time series with included break(s).…”

Section: Introductionmentioning

confidence: 99%

Challenges on modeling time series of basic tourist parameters in the Republic of Macedonia

2018

HORIZONS

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The basic parameters in development of tourism in the Republic of Macedonia, number of arrived tourists and number of spent nights have accentuated seasonal component and trend for foreign tourist. Besides the fact that these series have seasonal components, in some cases we can find structural break in the series. All these obstacles can be overcome if we choose appropriate model for basic tourist parameters. In these paper we present some of the series from regions in the Republic of Macedonia. We have chosen relevant regions that affects the development of tourism in the Republic of Macedonia. We made models for the series and also we have tested all the models for validity and stability. For created model we have tested the model with in sample and out of sample forecast of the series.

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Does Money Matter in Inflation Forecasting?

Binner

Tiňo²,

Tepper³

et al. 2010

SSRN Journal

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This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression -techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation.

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Comparative Analysis on Time Series With Included Structural Break

Cited by 3 publications

References 12 publications

Modelling, Forecasting and Testing Decisions for Seasonal Time Series in Tourism

Modelling, Forecasting and Testing Decisions for Seasonal Time Series in Tourism

Challenges on modeling time series of basic tourist parameters in the Republic of Macedonia

Does Money Matter in Inflation Forecasting?

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