Filter Design for the Seasonal Adjustment of a Time Series

Abstract 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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Intelligent POIs Recommender System Based on Time Series Analysis with Seasonal Adjustment

International Journal for Applied Information Management ◽

10.47738/ijaim.v2i2.28 ◽

2021 ◽

Vol 2 (2) ◽

pp. 66-80

Author(s):

Meng-Kuan Chen ◽

Hsin-Wen Wei ◽

Wei-Tsong Lee

Keyword(s):

Time Series ◽

Recommender Systems ◽

Recommender System ◽

Research Articles ◽

Seasonal Adjustment ◽

Huge Amount ◽

Travel Information ◽

Seasonal Factor ◽

Correct Location ◽

Travel Recommendation

Recommender systems have been applied on a variety of applications including movies, music, news, books, research articles, search queries, and travel information. Instead of searching travel information from the extremely huge amount of travel data, a personalized travel recommender system is desired. However, an inappropriate travel recommendation may result from a wrong season, even if it is already a correct location. The current recommender systems from time to time make an inappropriate commendation without considering the seasonal factor. In order to resolve the discrepancy, the seasonal factor should have been taken into consideration when making a good travel recommender system. Therefore, this study has taken the trend analysis, time series, and seasonal factor into considerations to cope with the above mentioned discrepancy and to make the travel recommender system renders a better fit.

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Recovering cointegration via wavelets in the presence of non-linear patterns

Studies in Nonlinear Dynamics & Econometrics ◽

10.1515/snde-2018-0120 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Jorge Martínez Compains ◽

Ignacio Rodríguez Carreño ◽

Ramazan Gençay ◽

Tommaso Trani ◽

Daniel Ramos Vilardell

Keyword(s):

Time Series ◽

Monte Carlo ◽

Gross National Product ◽

Structural Breaks ◽

Low Frequency ◽

Cointegration Test ◽

Seasonal Adjustment ◽

Traditional Use ◽

Monte Carlo Experiments ◽

Non Linear

Abstract Johansen’s Cointegration Test (JCT) performs remarkably well in finding stable bivariate cointegration relationships. Nonetheless, the JCT is not necessarily designed to detect such relationships in presence of non-linear patterns such as structural breaks or cycles that fall in the low frequency portion of the spectrum. Seasonal adjustment procedures might not detect such non-linear patterns, and thus, we expose the difficulty in identifying cointegrating relations under the traditional use of JCT. Within several Monte Carlo experiments, we show that wavelets can empower more the JCT framework than the traditional seasonal adjustment methodologies, allowing for identification of hidden cointegrating relationships. Moreover, we confirm these results using seasonally adjusted time series as US consumption and income, gross national product (GNP) and money supply M1 and GNP and M2.

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TESS: system for automatic seasonal adjustment and forecasting of time series

COMPSTAT ◽

10.1007/978-3-642-57678-2_52 ◽

2000 ◽

pp. 391-396

Author(s):

Albert Prat ◽

Victor Gomez ◽

Ignasi Solé ◽

Josep M. Catot

Keyword(s):

Time Series ◽

Seasonal Adjustment

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Soft Computing Based Statistical Time Series Analysis, Characterization of Chaos Theory, and Theory of Fractals

Handbook of Research on Computational Intelligence for Engineering, Science, and Business ◽

10.4018/978-1-4666-2518-1.ch002 ◽

2013 ◽

pp. 30-61 ◽

Cited By ~ 1

Author(s):

Mofazzal H. Khondekar ◽

Dipendra N. Ghosh ◽

Koushik Ghosh ◽

Anup Kumar Bhattacharya

Keyword(s):

Time Series ◽

Fractal Dimension ◽

Time Series Analysis ◽

Soft Computing ◽

Chaos Theory ◽

Filter Design ◽

Theoretical Perspective ◽

Series Analysis ◽

Statistical Time

The present work is an attempt to analyze the various researches already carried out from the theoretical perspective in the field of soft computing based time series analysis, characterization of chaos, and theory of fractals. Emphasis has been given in the analysis on soft computing based study in prediction, data compression, explanatory analysis, signal processing, filter design, tracing chaotic behaviour, and estimation of fractal dimension of time series. The present work is a study as a whole revealing the effectiveness as well as the shortcomings of the various techniques adapted in this regard.

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