Time Series Analysis and Its Applications

Shumway, Robert H.; Stoffer, David S.

doi:10.1007/978-3-319-52452-8

Cited by 931 publications

(482 citation statements)

References 129 publications

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“…Lagged values of surface soil moisture were correlated with instantaneous values at the subsurface. A maximum cross-correlation at negative lags indicated that surface soil moisture is leading subsurface soil moisture, and vice versa (Shumway and Stoffer, 2010). A 10-day lag was deemed long enough to show the presence of lag-lead relations in the time series since the maximum correlation occurred within this period.…”

Section: Analysis Of Lagged Dependence 321 Cross-correlationmentioning

confidence: 99%

“…However, cross-correlation is limited to providing a single value throughout the range of soil moisture encountered per lag. Furthermore, crosscorrelation generally aims to evaluate the strength of lagged linear dependence between two variables (Shumway and Stoffer, 2010). However, lagged dependence between surface and subsurface soil moisture may not be linear given that nonlinear processes determine water flow along the soil profile.…”

Section: Introductionmentioning

confidence: 99%

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Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values

Carranza

Ploeg

Torfs

2018

Hydrol. Earth Syst. Sci.

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Abstract. Recent advances in radar remote sensing popularized the mapping of surface soil moisture at different spatial scales. Surface soil moisture measurements are used in combination with hydrological models to determine subsurface soil moisture values. However, variability of soil moisture across the soil column is important for estimating depthintegrated values, as decoupling between surface and subsurface can occur. In this study, we employ new methods to investigate the occurrence of (de)coupling between surface and subsurface soil moisture. Using time series datasets, lagged dependence was incorporated in assessing (de)coupling with the idea that surface soil moisture conditions will be reflected at the subsurface after a certain delay. The main approach involves the application of a distributed-lag nonlinear model (DLNM) to simultaneously represent both the functional relation and the lag structure in the time series. The results of an exploratory analysis using residuals from a fitted loess function serve as a posteriori information to determine (de)coupled values. Both methods allow for a range of (de)coupled soil moisture values to be quantified. Results provide new insights into the decoupled range as its occurrence among the sites investigated is not limited to dry conditions.

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Section: Analysis Of Lagged Dependence 321 Cross-correlationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values

Carranza

Ploeg

Torfs

2018

Hydrol. Earth Syst. Sci.

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“…In modelling practice, time-series pre-processing is executed taking advantage of independent tools with statistical capabilities like Microsoft Excel, Matlab [10] and R [11] or, when the modeler has programming capability, making use of specific libraries in different coding languages like PANDAS [12] for Python or ROOT (https:// root.cern.ch/) for C ??. A popular time series analyses tool supporting calibration of hydrological models is TSPROC (Time Series PROCessor; [13]), which is a simple scripting language including features often used with surface-water models.…”

Section: Data Integration Modulementioning

confidence: 99%

Open source GIS platform for water resource modelling: FREEWAT approach in the Lugano Lake

2017

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The FREEWAT platform is an innovative Free and Open Source water resource modelling platform integrated in the QGIS geospatial software, using the SpatiaLite database, and including globally-established simulation codes from the USGS MODFLOW models family. This paper demonstrates its application to the Lugano Lake basin case study, Switzerland and Italy. Two specific modules of the platform were used to execute data integration and analyses: the Observation Analysis Tool and the Lake Package. The first one is a newly developed module facilitating the integration of time-series observations into modelling by enabling pre-and post-processing in the model environment; the latter is an existing MOD-FLOW package allowing dynamic evaluation of groundwater/lakes interaction. In the case study implementation, a participatory approach was adopted to enhance trust and acceptance of results. These show that integration of simulation codes within GIS is highly appreciated. Furthermore, its openness and freeness allow easily sharing of developed analysis and models. Stakeholders also positively evaluated the participatory process as it empowers decision making with a better understanding of model results and uncertainties. The combination of the FREE-WAT platform and the participatory approach may constitute a valuable methodology to include scientifically based analysis to be used for policy design and implementation.

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“…Both, the autocovariance function and the spectral density express the same information, but while the first one expresses it in terms of lags, the PSD expresses it in terms of cycles. [27] The periodogram is the sample-based counterpart of the power spectrum, and it is a tool used for the estimation of the PSD. [27] In this work, we used the Welch's averaged modified periodogram method to estimate the PSD using Hanningwindowed epochs of length 500 ms with an overlap of 250 ms.…”

Section: Feature Extraction: Power Spectral Densitymentioning

confidence: 99%

Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals

Vega

Sajed

Mathewson

et al. 2016

AIR

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Recognition of motor imagery tasks (MI) from electroencephalographic (EEG) signals is crucial for developing rehabilitation and motor assisted devices based on brain-computer interfaces (BCI). Here we consider the challenge of learning a classifier, based on relevant patterns of the EEG signals; this learning step typically involves both feature selection, as well as a base learning algorithm. However, in many cases it is not clear what combination of these methods will yield the best classifier. This paper contributes a detailed assessment of feature selection techniques, viz., squared Pearson's correlation (R 2 ), principal component analysis (PCA), kernel principal component analysis (kPCA) and fast correlation-based filter (FCBF); and the learning algorithms: linear discriminant analysis (LDA), support vector machines (SVM), and Feed Forward Neural Network (NN). A systematic evaluation of the combinations of these methods was performed in three two-class classification scenarios: rest vs. movement, upper vs. lower limb movement and right vs. left hand movement. FCBF in combination with SVM achieved the best results with a classification accuracy of 81.45%, 77.23% and 68.71% in the three scenarios, respectively. Importantly, FCBF determines, based on the feature set, whether a classifier can be learned, and if so, automatically identifies the subset of relevant and non-correlated features. This suggests that FCBF is a powerful method for BCI systems based on MI. Knowledge gained here about procedural combinations has the potential to produce useful BCI tool, that can provide effective motor control for the users.

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Time Series Analysis and Its Applications

Cited by 931 publications

References 129 publications

Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values

Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values

Open source GIS platform for water resource modelling: FREEWAT approach in the Lugano Lake

Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals

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