Trend Extraction for Seasonal Time Series Using Ensemble Empirical Mode Decomposition (original) (raw)
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Trend filtering via empirical mode decompositions
Computational Statistics & Data Analysis, 2013
The present work is concerned with the problem of extracting low-frequency trend from a given time series. To solve this problem, the authors develop a nonparametric technique called empirical mode decomposition (EMD) trend filtering. A key assumption is that the trend is representable as the sum of intrinsic mode functions produced by the EMD. Based on an empirical analysis of the EMD, the authors propose an automatic procedure for selecting the requisite intrinsic mode functions. To illustrate the effectiveness of the technique, the authors apply it to simulated time series containing different types of trend, as well as real-world data collected from an environmental study (atmospheric carbon dioxide levels at Mauna Loa Observatory) and from a large-scale bicycle rental service (rental numbers of Grand Lyon Vélo'v).
Energies
In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are e...
The Indian Journal of Agricultural Sciences
Due to multifaceted nature of agricultural price series, conventional mono-scale smoothing approaches are unable to catch its nonstationary and nonlinear properties. Recently, empirical mode decomposition (EMD) has been proposed as a new tool for time-frequency analysis method, which adaptively represents nonstationary signals as sum of different components. The essence of EMD is to decompose a time series into a sum of intrinsic mode function (IMF) components with individual intrinsic time scale properties. One of the major drawbacks of the EMD is the frequent appearance of mode mixing. Ensemble EMD (EEMD) is a substantial improvement of EMD which can better separate the scales naturally by adding white noise series to the original time series and then treating the ensemble averages as the true intrinsic modes. In this paper, daily price data of potato in Bangalore and Delhi markets are decomposed into eight independent intrinsic modes and one residue with different frequencies, in...
Communications in Computer and Information Science, 2019
Global warming is a real threat for the survival of life on Earth in the following 80 years. The effects of Global Warming are particularly harmful for inhabitants of very saturated urban settlements, which is the case of Mexico City. In this work, we analyse temperature time series from Mexico City Downtown, taken hourly between 1986 and 2019. The gaps in the time series were interpolated through the kriging method. Then, temporal tendencies and main frequencies were obtained through Empirical Mode Decomposition. The first frequency mode reveals a clear increasing tendency driven by Global Warming, which for 2019 was of 0.72 • C above a 30-year baseline period mean between 1986 and 2016. Furthermore, the shorter periods identified in the first intrinsic mode functions are likely driven by the solar activity periods. It remains to find the origin of the smallest identified periods in the time series (<0.36 years).
Empirical mode decomposition analysis of two different financial time series and their comparison
Chaos, Solitons & Fractals, 2008
Analysis of financial time series with,a view to understanding its underlying characteristic features has been the recent focus of scientists and practitioners studying the financial market. One of the key attributes of a time series is its periodicity. Because of their quasi-periodic nature, the financial time series do not reveal their periodicity clearly. One of the recent developments in time signal analysis is the Hilbert-Huang empirical mode decomposition (EMD) method, which elegantly brings out the underlying periodicity of any time-series. Not many efforts have been made to utilise this technique in qualitative analysis of financial time series. In the present study, we have used the EMD technique to analyse two different financial time series, viz., the daily movement of NIFTY index value of National Stock Exchange, India, and that of Hong Kong AOI, Hong Kong Stock Exchange from July 1990 to January 2006. The returns of the two indices are shown to have strikingly similar probability distribution. The IMF phase and amplitude probability distribution of the two indices also reveal striking similarity. This indicates a remarkable similarity of trading behaviour in the two markets. Considering the geographical and political separation of the two, this indeed is an important discovery.
ICEEMDAN, a variant of Empirical Mode Decomposition (EMD), is used to extract temperature cycles with periods from half a year to multiple decades from the \nobreak{HadCRUT5} global temperature anomaly data. The residual indicates an overall warming trend. The analysis is repeated for the Southern and Northern Hemispheres as well as the Tropics, defined as areas lying at or below 30 degrees of latitude. Multiannual cycles explain the apparently anomalous pause in global warming starting around 2000. The previously identified multidecadal cycle is found to be the most energetic and to account for recent global warming acceleration, beginning around 1993. This cycle's amplitude is found to be more variable than by previous work. Moreover, this variability varies by latitude. Sea ice loss acceleration is proposed as an explanation for global warming acceleration.
Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series
Water Resources Management, 2014
ABSTRACT In this study, a nonparametric technique to set up a river stage forecasting model based on empirical mode decomposition (EMD) is presented. The approach is based on the use of the EMD and artificial neural networks (ANN) to forecast next month's monthly streamflows. The proposed approach is applied to a real case study. The data from station on the Kizilirmak River in Turkey was used. The mean square errors (MSE), mean absolute errors (MAE) and correlation coefficient (R) statistics were used for evaluating the accuracy of the EMD-ANN model. The accuracy of the EMD-ANN model was then compared to the artificial neural networks (ANN) model. The results showed that EMD-ANN approach performed better than the ANN in predicting stream flows. The most accurate EMD-ANN model had MSE=0.0132, MAE=0.0883 and R=0.8012 statistics, respectively.
In this paper an algorithm of Enhanced Ensemble Empirical Mode Decomposition (EEEMD) is presented. Empirical Mode Decomposition (EMD) is an adaptive algorithm used for analyzing non linear and non stationary data which works by breaking the signal into a number of amplitude and frequency modulated (AM/FM) zero mean signals which are termed as Intrinsic Mode Functions(IMFs).but EMD experiences " Mode mixing " problem To overcome this problem Ensemble Empirical Mode Decomposition (EEMD) was proposed. The EEMD approach performs the EMD over an ensemble of original signal consists of sifting an ensemble of white noise added signal and treats the mean as the final true result. This approach will put an end to EMD mode mixing problem, however EEMD produced results does not satisfy the strict definition of IMF. To overcome this drawback, in the method here proposed, a unique residue is computed by adding noise at each stage of decomposition to obtain each IMF. The resulting decomposition is complete, with a numerically negligible error. Two examples are presented: a discrete Dirac delta function and an electrocardiogram signal. When compared with EEMD the new method here presented needs lesser number of iterations, thereby reducing the computational cost and an exact signal reconstruction, which is not possible with EEMD.
Journal of Climate, 2008
A simple guide to the new technique of empirical mode decomposition (EMD) in a meteorologicalclimate forecasting context is presented. A single application of EMD to a time series essentially acts as a local high-pass filter. Hence, successive applications can be used to produce a bandpass filter that is highly efficient at extracting a broadband signal such as the Madden-Julian oscillation (MJO). The basic EMD method is adapted to minimize end effects, such that it is suitable for use in real time. The EMD process is then used to efficiently extract the MJO signal from gridded time series of outgoing longwave radiation (OLR) data.
A new approach for crude oil price analysis based on Empirical Mode Decomposition
Energy Economics, 2008
The importance of understanding the underlying characteristics of international crude oil price movements attracts much attention from academic researchers and business practitioners. Due to the intrinsic complexity of the oil market, however, most of them fail to produce consistently good results. Empirical Mode Decomposition (EMD), recently proposed by Huang et al., appears to be a novel data analysis method for nonlinear and non-stationary time series. By decomposing a time series into a small number of independent and concretely implicational intrinsic modes based on scale separation, EMD explains the generation of time series data from a novel perspective. Ensemble EMD (EEMD) is a substantial improvement of EMD which can better separate the scales naturally by adding white noise series to the original time series and then treating the ensemble averages as the true intrinsic modes. In this paper, we extend EEMD to crude oil price analysis. First, three crude oil price series with different time ranges and frequencies are decomposed into several independent intrinsic modes, from high to low frequency. Second, the intrinsic modes are composed into a fluctuating process, a slowly varying part and a trend based on fine-to-coarse reconstruction. The economic meanings of the three components are identified as short term fluctuations caused by normal supply-demand disequilibrium or some other market activities, the effect of a shock of a significant event, and a long term trend. Finally, the EEMD is shown to be a vital technique for crude oil price analysis.