Seasonal Adjustment and Signal Extraction Time Series

Gómez, Víctor; Maravall, Agustín

doi:10.1002/9781118032978.ch8

Cited by 39 publications

(30 citation statements)

References 66 publications

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“…This method, as well as other programs of the 'X-11 family', consists of a moving average procedure for seasonally adjusting series (it is fully explained in Findley et al 1998). The frequency domain properties of the X-11 method have been discussed in the works of Wallis (1982), Bell and Monsell (1992), Dagum et al (1996), Gómez and Maravall (2001), Findley and Martin (2006) and others. Here, the X-11 procedure will be briefly discussed for the purpose of introducing its transfer function, which will be instrumental in analysing the X-11 behaviour in the presence of moving seasonality.…”

Section: The X-11 Methods In the Frequency Domainmentioning

confidence: 99%

Design of Seasonal Adjustment Filter Robust to Variations in the Seasonal Behaviour of Time Series

Martelotte

Souza

Silva

2017

Journal of Official Statistics

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Considering that many macroeconomic time series present changing seasonal behaviour, there is a need for filters that are robust to such changes. This article proposes a method to design seasonal filters that address this problem. The design was made in the frequency domain to estimate seasonal fluctuations that are spread around specific bands of frequencies.We assessed the generated filters by applying them to artificial data with known seasonal behaviour based on the ones of the real macroeconomic series, and we compared their performance with the one of X-13A-S. The results have shown that the designed filters have superior performance for series with pronounced moving seasonality, being a good alternative in these cases.

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Section: The X-11 Methods In the Frequency Domainmentioning

confidence: 99%

Design of Seasonal Adjustment Filter Robust to Variations in the Seasonal Behaviour of Time Series

Martelotte

Souza

Silva

2017

Journal of Official Statistics

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“…We fall in a particular case of a theoretical example, random walk plus white noise, used by Pierce (1979), but our treatment is more general than in Maravall (1986), Gómez and Maravall (2001b) and others. 7 Let 5 The computations in SEATS are not precisely performed that way.…”

Section: Arima Modellingmentioning

confidence: 99%

“…This (formal) power series expansion is denoted ξ(B, B −1 ) in Gómez and Maravall (2001b), such that p t = ξ(B, B −1 )e t . In Sect.…”

Section: Moving Average Representation and Wiener-kolmogorov Filtersmentioning

confidence: 99%

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On some remarks about SEATS signal extraction

Mélard

2015

SERIEs

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In seasonal adjustment a time series is considered as a juxtaposition of several components, the trend-cycle, and the seasonal and irregular components. The Bureau of the Census X-11 method, based on moving averages, correction of large errors and trading day adjustments, has long dominated. With the success of ARIMA modelling at the end of the 20th century, methods with better outlier detection and trading day corrections by regression with ARIMA errors have appeared, with the regARIMA module of Census X-12-ARIMA or Bank of Spain TRAMO-SEATS. SEATS consists of extracting the components by an ARIMA-model-based unobserved components approach. This means that models are used for each component such that the sum of the components is compatible with the ARIMA model for the corrected time series. The underlying theory of the SEATS program is studied in many papers but there is no complete and systematic description of its output. Our purpose is to examine SEATS text output and to explain the results in simple words and formulas. This is done on a simple example, a time series with a non-seasonal model so that the computations can be verified step by step. The principles behind SEATS are first described, including the admissible decompositions and the canonical decomposition, and the derivation of the Wiener-Kolmogorov filter. Then the example is introduced: the interest rates of US certificates of deposits. The text output from SEATS is presented in edited form in several tables. Finally, the main results are checked on the example by means of a Microsoft Excel workbook and direct computations. In particular, the forecasts and backcasts are obtained; the admissible and canonical decompositions with two components are discussed; the filters are first derived using autocorrelations of two auxiliary ARMA processes, then applied on the prolonged time series; and the characteristics of the estimates, the revisions and the growth rates are analyzed.

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“…• ARIMA Models (Box & Jenkins, 1970): Tramo/Seats (Gomez & Maravall, 2001) The "classical" calculation models have been used in these models. For those which require the estimation of one parameter, or of several, mathematical programming models have been used (determinist and fuzzy) which minimise within-sample errors or out-ofsample errors.…”

Section: Application To a Real Casementioning

confidence: 99%

Forecasting model selection through out-of-sample rolling horizon weighted errors

Mula

2011

Expert Systems with Applications

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ElsevierPoler Escoto, R.; Mula, J. (2011) . AbstractDemand Forecasting is an essential process for any firm whether it is a supplier, manufacturer or retailer. A large number of research works about time series forecast techniques exists in the literature, and there are many time series forecasting tools. In many cases, however, selecting the best time series forecasting model for each time series to be dealt with is still a complex problem. In this paper, a new automatic selection procedure of time series forecasting models is proposed. The selection criterion has been tested using the set of monthly time series of the M3 Competition and two basic forecasting models obtaining interesting results. This selection criterion has been implemented in a forecasting expert system and applied to a real case, a firm that produces steel products for construction, which automatically performs monthly forecasts on tens of thousands of time series. As result, the firm has increased the level of success in its demand forecasts.

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Seasonal Adjustment and Signal Extraction Time Series

Cited by 39 publications

References 66 publications

Design of Seasonal Adjustment Filter Robust to Variations in the Seasonal Behaviour of Time Series

Design of Seasonal Adjustment Filter Robust to Variations in the Seasonal Behaviour of Time Series

On some remarks about SEATS signal extraction

Forecasting model selection through out-of-sample rolling horizon weighted errors

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