An Integrated SSA-ARIMA Approach to Make Multiple Day Ahead Forecasts for the Daily Maximum Ambient O3 Concentration

An integrated approach that combines a signal extraction method SSA (Singular Spectrum Analysis) with ARIMA (Autoregressive Integrated Moving Average) has been introduced to make multiple day ahead forecasts for daily maximum ambient O_3 concentrations. The results are compared with the forecasts obtained by more commonly used signal extraction method FFT (Fast Fourier Transforms) in combination with ARIMA. The data from six different European AIRBASE stations have been analyzed to test their skills in one-day ahead to five-day ahead forecasts. In SSA-ARIMA, the SSA has been used to extract the different structural components of the time series. The first few principal components have been selected based on their dominance in the eigenvalues spectrum. Each component has been modelled separately and their sum is called SSA-component. The residuals of the ＂SSA-component subtracted from the actual data＂ exhibited stationarity and have been modeled using ARIMA process. In the second approach called by FFT-ARIMA, FFT has been used to model the periodicities exhibited by the time series. The first few predominant frequencies have been selected based on the power-spectrum characteristics of the time series. The residuals of the ＂FFT-component subtracted from the actual data＂ were modeled using ARIMA process. The performances of both the SSA-ARIMA and FFT-ARIMA were evaluated for 30 out of sample days for one to five day ahead forecasts. The statistical indicators RMSE (root mean square error) and MAE (mean absolute error) reveal that the SSA-ARIMA consistently outperforms FFT-ARIMA. Moreover, SSA-ARIMA has also been found to perform better especially for multiple day ahead forecasts. The results clearly indicate that the SSA-ARIMA approach can be a potential alternative to make short term air quality forecasting.

[1]  Ari Karppinen,et al.  Evaluation of a multiple regression model for the forecasting of the concentrations of NOx and PM10 in Athens and Helsinki. , 2011, The Science of the total environment.

[2]  Vikas Singh,et al.  A cokriging based approach to reconstruct air pollution maps, processing measurement station concentrations and deterministic model simulations , 2011, Environ. Model. Softw..

[3]  C. Willmott.  Some Comments on the Evaluation of Model Performance , 1982 .

[4]  James L. Powell,et al.  Time Series Models , 2021, Stochastic Limit Theory.

[5]  K. Ridder,et al.  GARCH modelling in association with FFT–ARIMA to forecast ozone episodes , 2010 .

[6]  Giorgio Corani,et al.  Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning , 2005 .

[7]  Dong-Sool Kim,et al.  A new method of ozone forecasting using fuzzy expert and neural network systems. , 2004, The Science of the total environment.

[8]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[9]  Ujjwal Kumar,et al.  A Wavelet-based Neural Network Model to Predict Ambient Air Pollutants’ Concentration , 2011 .

[10]  I. K. Larissi,et al.  Application of Multiple Linear Regression Models and Artificial Neural Networks on the Surface Ozone Forecast in the Greater Athens Area, Greece , 2012 .

[11]  Michael Frankfurter,et al.  Numerical Recipes In C The Art Of Scientific Computing , 2016 .

[12]  Sukumar Devotta,et al.  Air quality forecasting using a hybrid autoregressive and nonlinear model , 2006 .

[13]  J. Elsner,et al.  Singular Spectrum Analysis: A New Tool in Time Series Analysis , 1996 .

[14]  J. Carretero,et al.  Stochastic model to forecast ground-level ozone concentration at urban and rural areas. , 2005, Chemosphere.

[15]  R. Vautard,et al.  Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series , 1989 .

[16]  W. Geoffrey Cobourn,et al.  Fuzzy system models combined with nonlinear regression for daily ground-level ozone predictions , 2007 .

[17]  Monnie McGee,et al.  Introduction to Time Series Analysis and Forecasting: With Applications of SAS and SPSS , 2000 .

[18]  Mohd Talib Latif,et al.  Long term assessment of air quality from a background station on the Malaysian Peninsula. , 2014, The Science of the total environment.

[19]  Richard A. Davis,et al.  Introduction to time series and forecasting , 1998 .

[20]  G. P. King,et al.  Extracting qualitative dynamics from experimental data , 1986 .

[21]  Brent R. Young,et al.  Fuzzy logic modeling of surface ozone concentrations , 2005, Comput. Chem. Eng..

[22]  Mohd Talib Latif,et al.  An assessment of influence of meteorological factors on PM10 and NO2 at selected stations in Malaysia , 2012 .

[23]  Robert H. Shumway,et al.  Time series analysis and its applications : with R examples , 2017 .

[24]  I. Burhan Türksen,et al.  Fuzzy System Models , 2009, Encyclopedia of Complexity and Systems Science.

[25]  Xiekang Wang,et al.  A preliminary study of ozone trend and its impact on environment in Hong Kong. , 2002, Environment international.

[26]  J. Kukkonen,et al.  Intercomparison of air quality data using principal component analysis, and forecasting of PM₁₀ and PM₂.₅ concentrations using artificial neural networks, in Thessaloniki and Helsinki. , 2011, The Science of the total environment.

[27]  Anna Pederzoli,et al.  Performance criteria to evaluate air quality modeling applications , 2012 .

[28]  Ujjwal Kumar,et al.  Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India , 2010 .

[29]  Y. Leea,et al.  Ozone episodes in urban Hong Kong 1994 – 1999 , 2002 .

[30]  Ujjwal Kumar,et al.  ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO) , 2010 .

[31]  Wei-Zhen Lu,et al.  Performance assessment of air quality monitoring networks using principal component analysis and clu , 2011 .

[32]  Michael Ghil,et al.  ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES , 2002 .

[33]  R. Vautard,et al.  Singular-spectrum analysis: a toolkit for short, noisy chaotic signals , 1992 .

[34]  M. Odman,et al.  Analysis of surface ozone and nitrogen oxides at urban, semi-rural and rural sites in Istanbul, Turkey. , 2013, The Science of the total environment.

[35]  Li-Chiu Chang,et al.  Forecasting of ozone episode days by cost-sensitive neural network methods. , 2009, The Science of the total environment.

[36]  Wei-Zhen Lu,et al.  Ground-level ozone prediction by support vector machine approach with a cost-sensitive classification scheme. , 2008, The Science of the total environment.

[37]  C. Willmott.  ON THE VALIDATION OF MODELS , 1981 .

[38]  A. Zhigljavsky,et al.  Forecasting European industrial production with singular spectrum analysis , 2009 .

[39]  Anatoly Zhigljavsky,et al.  Predicting daily exchange rate with singular spectrum analysis , 2010 .

[40]  Elizabeth Ann Maharaj,et al.  Autoregressive model-based fuzzy clustering and its application for detecting information redundancy in air pollution monitoring networks , 2013, Soft Comput..

[41]  J. Elsner.  Analysis of Time Series Structure: SSA and Related Techniques , 2002 .

[42]  A. Harvey.  Time series models , 1983 .

[43]  Murat Kulahci,et al.  Introduction to Time Series Analysis and Forecasting , 2008 .

[44]  I. Burhan Turksen.  Fuzzy System Models. , 2009 .

[45]  Yves Candau,et al.  Hourly ozone prediction for a 24-h horizon using neural networks , 2008, Environ. Model. Softw..

[46]  José David Martín-Guerrero,et al.  Neural networks for analysing the relevance of input variables in the prediction of tropospheric ozone concentration , 2006 .

[47]  N Moussiopoulos,et al.  Statistical analysis of environmental data as the basis of forecasting: an air quality application. , 2002, The Science of the total environment.

[48]  Mohd Talib Latif,et al.  Spatial Assessment of Air Quality Patterns in Malaysia Using Multivariate Analysis , 2012 .

[49]  Claudio Carnevale,et al.  An integrated air quality forecast system for a metropolitan area. , 2011, Journal of environmental monitoring : JEM.