Applying Time Series Models for Flight Data Insights: Prediction, MSE Analysis, and Anomaly Detection
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Timeseries Forcasting↗
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Results for “Time Series Data”
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Impact of Tile Drainage on Evapotranspiration in South Dakota, USA, Based on High Spatiotemporal Resolution Evapotranspiration Time Series From a Multisatellite Data Fusion System
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Yun Yang↗
Characterizing Spatiotemporal Uncertainty in Interpolated Meteorological Data
Interpolated meteorological data invariably contain errors. These errors have structure in time and space, particularly autocorrelation, which can cause the effects of errors to compound when model outputs are aggregated temporally or spatially. One way to account for this uncertainty is with a probabilistic model from which samples can be drawn that are coherent with respect to underlying spatial and temporal covariance structure. This work describes a probabilistic method for spatial interpolation of point-wise meteorological time series. Observational data from weather stations are generally sparse in space and dense in time (but sometimes missing). The method works by projecting time series onto orthogonal basis vectors and spatially interpolating each resulting component independently. Under suitable assumptions, and data transformations to better satisfy those assumptions, Gaussian process regression provides a complete description of the joint predictive distribution over a Gaussian random field. Spatiotemporally coherent realizations are generated as the sum of conditional (spatial) simulations of each orthogonal (temporal) component. Data-derived and generic orthogonal bases are considered. In addition to spatial interpolation, imputation of missing observational data is examined. The method is applied using near-surface air temperature over the Western United States and validated by comparing theoretical versus actual coverage of predictive distributions and analyzing the degree to which spatial and temporal covariance structure is reproduced. Computational considerations, relating to conditional simulation of random fields, are also addressed.
Conor T Doherty↗
Application of the Hilbert-Huang Transform to Financial Data
A paper discusses the application of the Hilbert-Huang transform (HHT) method to time-series financial-market data. The method was described, variously without and with the HHT name, in several prior NASA Tech Briefs articles and supporting documents. To recapitulate: The method is especially suitable for analyzing time-series data that represent nonstationary and nonlinear phenomena including physical phenomena and, in the present case, financial-market processes. The method involves the empirical mode decomposition (EMD), in which a complicated signal is decomposed into a finite number of functions, called "intrinsic mode functions" (IMFs), that admit well-behaved Hilbert transforms. The HHT consists of the combination of EMD and Hilbert spectral analysis. The local energies and the instantaneous frequencies derived from the IMFs through Hilbert transforms can be used to construct an energy-frequency-time distribution, denoted a Hilbert spectrum. The instant paper begins with a discussion of prior approaches to quantification of market volatility, summarizes the HHT method, then describes the application of the method in performing time-frequency analysis of mortgage-market data from the years 1972 through 2000. Filtering by use of the EMD is shown to be useful for quantifying market volatility.
Huang, Norden↗
Tools and Data Services from the GSFC Earth Sciences DAAC for Aura Science Data Users
In these times of rapidly increasing amounts of archived data, tools and data services that manipulate data and uncover nuggets of information that potentially lead to scientific discovery are becoming more and more essential. The Goddard Space Flight Center (GSFC) Earth Sciences (GES) Distributed Active Archive Center (DAAC) has made great strides in facilitating science and applications research by, in consultation with its users, developing innovative tools and data services. That is, as data users become more sophisticated in their research and more savvy with information extraction methodologies, the GES DAAC has been responsive to this evolution. This presentation addresses the tools and data services available and under study at the GES DAAC, applied to the Earth sciences atmospheric data. Now, with the data from NASA's latest Atmospheric Chemistry mission, Aura, being readied for public release, GES DAAC tools, proven successful for past atmospheric science missions such as MODIS, AIRS, TRMM, TOMS, and UARS, provide an excellent basis for similar tools updated for the data from the Aura instruments. GES DAAC resident Aura data sets are from the Microwave Limb Sounder (MLS), Ozone Monitoring Instrument (OMI), and High Resolution Dynamics Limb Sounder (HIRDLS). Data obtained by these instruments afford researchers the opportunity to acquire accurate and continuous visualization and analysis, customized for Aura data, will facilitate the use and increase the usefulness of the new data. The Aura data, together with other heritage data at the GES DAAC, can potentially provide a long time series of data. GES DAAC tools will be discussed, as well as the GES DAAC Near Archive Data Mining (NADM) environment, the GIOVANNI on-line analysis tool, and rich data search and order services. Information can be found at: http://daac.gsfc.nasa.gov/upperatm/aura/. Additional information is contained in the original extended abstract.
Kempler, S.↗
Quantifying Surface Water Dynamics at 30 Meter Spatial Resolution in the North American High Northern Latitudes 1991-2011
The availability of a dense time series of satellite observations at moderate (30 m) spatial resolution is enabling unprecedented opportunities for understanding ecosystems around the world. A time series of data from Landsat was used to generate a series of three maps at decadal time step to show how surface water has changed from 1991 to 2011 in the high northern latitudes of North America. Previous attempts to characterize the change in surface water in this region have been limited in either spatial or temporal resolution, or both. This series of maps was generated for the NASA Arctic and Boreal Vulnerability Experiment (ABoVE), which began in fall 2015. These maps show a nominal extent of surface water by using multiple observations to make a single map for each time step. This increases the confidence that any detected changes are related to climate or ecosystem changes not simply caused by short duration weather events such as flood or drought. The methods and comparison to other contemporary maps of the region are presented here. Initial verification results indicate 96% producer accuracy and 54% user accuracy when compared to 2-m resolution World View-2 data. All water bodies that were omitted were one Landsat pixel or smaller, hence below detection limits of the instrument.
Carroll, Mark↗
A 40 Year Time Series of SBUV Observations: the Version 8.6 Processing
Under a NASA program to produce long term data records from instruments on multiple satellites (MEaSUREs), data from a series of eight SBUV and SBUV 12 instruments have been reprocessed to create a 40 year long ozone time series. Data from the Nimbus 4 BUV, Nimbus 7 SBUV, and SBUV/2 instruments on NOAA 9, 11, 14, 16, 17, and 18 were used covering the period 1970 to 1972 and 1979 to the present. In past analyses an ozone time series was created from these instruments by adjusting ozone itself, instrument by instrument, for consistency during overlap periods. In the version 8.6 processing adjustments were made to the radiance calibration of each instrument to maintain a consistent calibration over the entire time series. Data for all eight instruments were then reprocessed using the adjusted radiances. Reprocessing is necessary to produce an accurate latitude dependence. Other improvements incorporated in version 8.6 included the use of the ozone cross sections of Brion, Daumont, and Malicet, and the use of a cloud height climatology derived from Aura OMI measurements. The new cross sections have a more accurate temperature dependence than the cross sections previously used. The OMI-based cloud heights account for the penetration of UV into the upper layers of clouds. The consistency of the version 8.6 time series was evaluated by intra-instrument comparisons during overlap periods, comparisons with ground-based instruments, and comparisons with measurements made by instruments on other satellites such as SAGE II and UARS MLS. These comparisons show that for the instruments on NOAA 16, 17 and 18, the instrument calibrations were remarkably stable and consistent from instrument to instrument. The data record from the Nimbus 7 SBUV was also very stable, and SAGE and ground-based comparisons show that the' calibration was consistent with measurements made years laterby the NOAA 16 instrument. The calibrations of the SBUV/2 instruments on NOAA 9, 11, and 14 were more of a problem. The rapidly drifting orbits of these satellites resulted in relative time and altitude dependent differences that are significant. Despite these problems, total column ozone appears to be consistent to better than 1% over the entire time series, while the ozone vertical distribution is consistent to approximately 5%.
McPeters, Richard↗
Photometric Analysis in the Kepler Science Operations Center Pipeline
We describe the Photometric Analysis (PA) software component and its context in the Kepler Science Operations Center (SOC) pipeline. The primary tasks of this module are to compute the photometric flux and photocenters (centroids) for over 160,000 long cadence (~thirty minute) and 512 short cadence (~one minute) stellar targets from the calibrated pixels in their respective apertures. We discuss the science algorithms for long and short cadence PA: cosmic ray cleaning; background estimation and removal; aperture photometry; and flux-weighted centroiding. We discuss the end-to-end propagation of uncertainties for the science algorithms. Finally, we present examples of photometric apertures, raw flux light curves, and centroid time series from Kepler flight data. PA light curves, centroid time series, and barycentric timestamp corrections are exported to the Multi-mission Archive at Space Telescope [Science Institute] (MAST) and are made available to the general public in accordance with the NASA/Kepler data release policy.
Twicken, Joseph D.↗
Photometric Analysis in the Kepler Science Operations Center Pipeline
We describe the Photometric Analysis (PA) software component and its context in the Kepler Science Operations Center (SOC) Science Processing Pipeline. The primary tasks of this module are to compute the photometric flux and photocenters (centroids) for over 160,000 long cadence (~thirty minute) and 512 short cadence (~one minute) stellar targets from the calibrated pixels in their respective apertures. We discuss science algorithms for long and short cadence PA: cosmic ray cleaning; background estimation and removal; aperture photometry; and flux-weighted centroiding. We discuss the end-to-end propagation of uncertainties for the science algorithms. Finally, we present examples of photometric apertures, raw flux light curves, and centroid time series from Kepler flight data. PA light curves, centroid time series, and barycentric timestamp corrections are exported to the Multi-mission Archive at Space Telescope [Science Institute] (MAST) and are made available to the general public in accordance with the NASA/Kepler data release policy.
photocenter↗
Time series analysis of sferics rate data associated with severe weather patterns
Data obtained by an electronic transducer measuring the rate of occurrence of electrical disturbances in the atmosphere (the sferic rate in the form of a time series) over the life of electrical storms are analyzed. It is found that the sferic rate time series are not stationary. The sferics rate time series has a complete life cycle associated with a particular storm. The approach to recognition of a spectral pattern is somewhat similar to real-time recognition of the spoken word.
Wang, P. P.↗
How the Open Data Policy of the Landsat Program Has Advanced Our Understanding of Environmental Change
A time series is a sequence of observations of a phenomenon taken sequentially in time. A crucial characteristic of a time series is the dependence among adjacent observations – techniques for analyzing this dependence are referred to as time series analysis. This analytical approach enables us to predict or forecast future values of a time series, study the impact of various inputs on the observed phenomenon, and examine interrelationships among related time series variables. Within the geographical sciences, time series analysis has historically been limited to coarse-resolution satellite data, as constructing time series of data suitable for studying land cover and land use dynamics, such as Landsat data, were prohibitively costly. A transformative shift occurred in 2008 when the U.S. Government decided to make free and open all past and future data collected by the Landsat satellite program. The decision brought about a paradigm shift away from analyzing individual images or observations to continuous monitoring in time. Of particular relevance to environmental remote sensing is the ability to forecast observations – if we can predict how future observations should behave, we can infer information about how the land surface is changing. In this presentation, we will examine literature examples that showcase scientific gains enabled by time series analysis of satellite data. We will delve into how the analysis of dense time series of satellite data revealed that overall rate of forest disturbance in the Amazon has increased despite a reduction in deforestation; how different types of forest degradation, previously unquantified, are now being accurately assessed in the Caucasus region; and how we now can study the highly dynamic and intricate patterns of shifting cultivation in Southeast Asia.
Pontus Olofsson↗
Modal Behavior of Hemispheric Sea Ice Covers
Recent papers have described 18-year trends and annual oscillations in the Arctic and Antarctic sea ice extents, areas, and enclosed open water areas based on a newly-formulated 18.2-year ice concentration time series. This time series includes data for the entire Arctic and Antarctic ice covers, as well as for previously defined subregions consisting of 5 sectors in the Antarctic and 9 regions in the Arctic. It was obtained by fine-tuning the sea ice algorithm tie points individually for each of the four sensors used to acquire the data. In this paper, we extend these analyses to an examination of the intrinsic modes of these time series, obtained by means of Empirical Mode Decomposition, with emphasis on periodicities greater than the annual cycle. Quasibiennial and quasiquadrennial oscillations observed with a different technique and reported earlier for the first 8.8 years of this time series were also observed in the present series. However, the intrinsic modes were not monochromatic; they feature frequency as well as amplitude modulation within their respective frequency bands. Modal periods of up to 18 years are observed, with important implications for the trend analyses published earlier. These results are compared with the oscillations in the Length-of-Day and North Atlantic Oscillation parameters similarly determined for the same 18.2-year period.
Gloersen, Per↗
Efficient hemodynamic event detection utilizing relational databases and wavelet analysis
Development of a temporal query framework for time-oriented medical databases has hitherto been a challenging problem. We describe a novel method for the detection of hemodynamic events in multiparameter trends utilizing wavelet coefficients in a MySQL relational database. Storage of the wavelet coefficients allowed for a compact representation of the trends, and provided robust descriptors for the dynamics of the parameter time series. A data model was developed to allow for simplified queries along several dimensions and time scales. Of particular importance, the data model and wavelet framework allowed for queries to be processed with minimal table-join operations. A web-based search engine was developed to allow for user-defined queries. Typical queries required between 0.01 and 0.02 seconds, with at least two orders of magnitude improvement in speed over conventional queries. This powerful and innovative structure will facilitate research on large-scale time-oriented medical databases.
NASA Discipline Cardiopulmonary↗
Adaptive Sensing of Time Series with Application to Remote Exploration
We address the problem of adaptive informationoptimal data collection in time series. Here a remote sensor or explorer agent throttles its sampling rate in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility -- all collected datapoints lie in the past, but its resource allocation decisions require predicting far into the future. Our solution is to continually fit a Gaussian process model to the latest data and optimize the sampling plan on line to maximize information gain. We compare the performance characteristics of stationary and nonstationary Gaussian process models. We also describe an application based on geologic analysis during planetary rover exploration. Here adaptive sampling can improve coverage of localized anomalies and potentially benefit mission science yield of long autonomous traverses.
artificial intelligence↗
Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations
This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it-an improved and generalized version of Bayesian Blocks [Scargle 1998]-that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piece- wise linear and piecewise exponential representations, multivariate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by [Arias-Castro, Donoho and Huo 2003]. In the spirit of Reproducible Research [Donoho et al. (2008)] all of the code and data necessary to reproduce all of the figures in this paper are included as auxiliary material.
signal detection↗
An investigation of ground-based observations of solar oscillations at Stanford
Data obtained in the last 8 years of solar differential Doppler observations at Stanford were considered. The four best time series of data were examined in detail. The sources of error in the data were investigated and removed where possible. In particular, the contribution resulting from transparency variations in the sky was examined. Detection method applicable to data with low signal to noise ratio and low filling factor were developed and utilized for the investigation of global solar modes of oscillations in the data. The frequencies of p-modes were measured and identified. The presence of g-modes were also determined in the Stanford data.
Henning, Harald M. J.↗
Real Data and Rapid Results: Ocean Color Data Analysis with Giovanni (GES DISC Interactive Online Visualization and ANalysis Infrastructure)
The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has taken a major step addressing the challenge of using archived Earth Observing System (EOS) data for regional or global studies by developing an infrastructure with a World Wide Web interface which allows online, interactive, data analysis: the GES DISC Interactive Online Visualization and ANalysis Infrastructure, or "Giovanni." Giovanni provides a data analysis environment that is largely independent of underlying data file format. The Ocean Color Time-Series Project has created an initial implementation of Giovanni using monthly Standard Mapped Image (SMI) data products from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission. Giovanni users select geophysical parameters, and the geographical region and time period of interest. The system rapidly generates a graphical or ASCII numerical data output. Currently available output options are: Area plot (averaged or accumulated over any available data period for any rectangular area); Time plot (time series averaged over any rectangular area); Hovmeller plots (image view of any longitude-time and latitude-time cross sections); ASCII output for all plot types; and area plot animations. Future plans include correlation plots, output formats compatible with Geographical Information Systems (GIs), and higher temporal resolution data. The Ocean Color Time-Series Project will produce sensor-independent ocean color data beginning with the Coastal Zone Color Scanner (CZCS) mission and extending through SeaWiFS and Moderate Resolution Imaging Spectroradiometer (MODIS) data sets, and will enable incorporation of Visible/lnfrared Imaging Radiometer Suite (VIIRS) data, which will be added to Giovanni. The first phase of Giovanni will also include tutorials demonstrating the use of Giovanni and collaborative assistance in the development of research projects using the SeaWiFS and Ocean Color Time-Series Project data in the online Laboratory for Ocean Color Users (LOCUS). The synergy of Giovanni with high-quality ocean color data provides users with the ability to investigate a variety of important oceanic phenomena, such as coastal primary productivity related to pelagic fisheries, seasonal patterns and interannual variability, interdependence of atmospheric dust aerosols and harmful algal blooms, and the potential effects of climate change on oceanic productivity.
Acker, J. G.↗
Shoshone River Water Resources II: Quantifying Sediment Input in the Shoshone River in Wyoming using the Soil and Water Assessment Tool for Enhanced Water Quality Monitoring
The Willwood Dam, an irrigation diversion dam located on the Shoshone River (Wyoming, USA), has faced ongoing issues with sediment accumulation and needs frequent sediment flushing to remain operable. However, high suspended sediment levels during flushing events have negatively impacted downstream aquatic ecology and recreational opportunities. To address these problems, DEVELOP partnered with the Wyoming Department of Environmental Quality, Shoshone River Partners, and United States Geologic Survey. During term one, the team developed a workflow to map turbidity using PlanetScope imagery, analyze time series precipitation data, and create landcover maps. For this term, we focused on three methods to gain a better understanding of sediment sources: 1) improving remote sensing of turbidity, 2) modeling sediment transport within the watershed using the Soil and Water Assessment Tool (SWAT), and 3) conducting a snow cover time series analysis using Suomi NPP VIIRS imagery. Through remote sensing, we found Dry Creek/Homesteader Creek and Penney Gulch/Rough Gulch had the highest concentration sediment plumes. The SWAT+ model created a high-resolution grid model of the watershed, identifying high sedimentation in the western and southern subbasins but displayed low-correlated calibration and validation results due to limited observed data. We analyzed the snow cover extent alongside other hydrologic variables and found that snow melt events correlated with increases in suspended sediment concentration and turbidity values. Coupling remote sensing with hydrological modeling will give watershed managers a new perspective on high-priority regions for implementation of remediation to reduce the sediment build-up at the Willwood Dam.
Suspended Sediment↗