scholarly journals Approaches in highly parameterized inversion: TSPROC, a general time-series processor to assist in model calibration and result summarization

10.3133/tm7c7 ◽  
2012 ◽  
Author(s):  
Stephen M. Westenbroek ◽  
John Doherty ◽  
John F. Walker ◽  
Victor A. Kelson ◽  
Randall J. Hunt ◽  
...  
Keyword(s):  
Time Series ◽  
Model Calibration ◽  
Series Processor ◽  
General Time

scholarly journals Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

2014 ◽  
Vol 18 (1) ◽  
pp. 353-365 ◽  
Author(s):  
U. Haberlandt ◽  
I. Radtke
Keyword(s):  
Time Series ◽  
Frequency Analysis ◽  
Model Calibration ◽  
Hydrological Model ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Rainfall Data ◽  
Flood Frequency Analysis ◽  
Peak Flows ◽  
Hourly Rainfall

Abstract. Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the application for derived flood frequency analysis.

scholarly journals Uniform nonparametric inference for time series using Stata

2020 ◽  
Vol 20 (3) ◽  
pp. 706-720
Author(s):  
Jia Li ◽  
Zhipeng Liao ◽  
Mengsi Gao
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Dynamic Equilibrium ◽  
Series Data ◽  
Conditional Moment ◽  
Specification Tests ◽  
Series Estimation ◽  
Moment Restrictions ◽  
Special Case ◽  
General Time

In this article, we introduce a command, tssreg, that conducts nonparametric series estimation and uniform inference for time-series data, including the case with independent data as a special case. This command can be used to nonparametrically estimate the conditional expectation function and the uniform confidence band at a user-specified confidence level, based on an econometric theory that accommodates general time-series dependence. The uniform inference tool can also be used to perform nonparametric specification tests for conditional moment restrictions commonly seen in dynamic equilibrium models.

scholarly journals Development of Indicator of Data Sufficiency for Feature-based Early Time Series Classification with Applications of Bearing Fault Diagnosis

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 790
Author(s):  
Gilseung Ahn ◽  
Hwanchul Lee ◽  
Jisu Park ◽  
Sun Hur
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Early Time ◽  
Time Series Classification ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Benchmark Datasets ◽  
Feature Based ◽  
General Time

Diagnosis of bearing faults is crucial in various industries. Time series classification (TSC) assigns each time series to one of a set of pre-defined classes, such as normal and fault, and has been regarded as an appropriate approach for bearing fault diagnosis. Considering late and inaccurate fault diagnosis may have a significant impact on maintenance costs, it is important to classify bearing signals as early and accurately as possible. TSC, however, has a major limitation, which is that a time series cannot be classified until the entire series is collected, implying that a fault cannot be diagnosed using TSC in advance. Therefore, it is important to classify a partially collected time series for early time series classification (ESTC), which is a TSC that considers both accuracy and earliness. Feature-based TSCs can handle this, but the problem is to determine whether a partially collected time series is enough for a decision that is still unsolved. Motivated by this, we propose an indicator of data sufficiency to determine whether a feature-based fault detection classifier can start classifying partially collected signals in order to diagnose bearing faults as early and accurately as possible. The indicator is trained based on the cosine similarity between signals that were collected fully and partially as input to the classifier. In addition, a parameter setting method for efficiently training the indicator is also proposed. The results of experiments using four benchmark datasets verified that the proposed indicator increased both accuracy and earliness compared with the previous time series classification method and general time series classification.

Time‐Series Analysis

2009 ◽  
Author(s):  
Jon Pevehouse ◽  
Jason D. Brozek
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Data Aggregation ◽  
Multivariate Analyses ◽  
Temporal Stability ◽  
Var Models ◽  
Series Analysis ◽  
History Of ◽  
General Time ◽  
Analysis Of Time Series

This article discusses time-series methods such as simple time-series regressions, ARIMA models, vector autoregression (VAR) models, and unit root and error correction models (ECM). It specifically presents a brief history of time-series analysis before moving to a review of the basic time-series model. It then describes the stationary models in univariate and multivariate analyses. The nonstationary models of each type are addressed. In addition, various issues regarding the analysis of time series including data aggregation and temporal stability are considered. Before concluding, the article briefly reports the time-series techniques in the context of panel data. In general, time-series analysis can help improve the understanding of the political world.

Time Series Processor Version 4.0

1985 ◽  
Vol 39 (2) ◽  
pp. 141
Author(s):  
TSP International
Keyword(s):  
Time Series ◽  
Series Processor

scholarly journals Information content of stream level class data for hydrological model calibration

2017 ◽  
Vol 21 (9) ◽  
pp. 4895-4905 ◽  
Author(s):  
H. J. Ilja van Meerveld ◽  
Marc J. P. Vis ◽  
Jan Seibert
Keyword(s):  
Time Series ◽  
Citizen Science ◽  
Model Calibration ◽  
Hydrological Model ◽  
Model Performance ◽  
Added Value ◽  
Distributed Data ◽  
Vertical Resolution ◽  
Level Data ◽  
Spatially Distributed

Abstract. Citizen science can provide spatially distributed data over large areas, including hydrological data. Stream levels are easier to measure than streamflow and are likely also observed more easily by citizen scientists than streamflow. However, the challenge with crowd based stream level data is that observations are taken at irregular time intervals and with a limited vertical resolution. The latter is especially the case at sites where no staff gauge is available and relative stream levels are observed based on (in)visible features in the stream, such as rocks. In order to assess the potential value of crowd based stream level observations for model calibration, we pretended that stream level observations were available at a limited vertical resolution by transferring streamflow data to stream level classes. A bucket-type hydrological model was calibrated with these hypothetical stream level class data and subsequently evaluated on the observed streamflow records. Our results indicate that stream level data can result in good streamflow simulations, even with a reduced vertical resolution of the observations. Time series of only two stream level classes, e.g. above or below a rock in the stream, were already informative, especially when the class boundary was chosen towards the highest stream levels. There was some added value in using up to five stream level classes, but there was hardly any improvement in model performance when using more level classes. These results are encouraging for citizen science projects and provide a basis for designing observation systems that collect data that are as informative as possible for deriving model based streamflow time series for previously ungauged basins.

scholarly journals Streamflow characteristics from modeled runoff time series – importance of calibration criteria selection

2017 ◽  
Vol 21 (11) ◽  
pp. 5443-5457 ◽  
Author(s):  
Sandra Pool ◽  
Marc J. P. Vis ◽  
Rodney R. Knight ◽  
Jan Seibert
Keyword(s):  
Time Series ◽  
Model Calibration ◽  
Performance Metrics ◽  
Low Flow ◽  
Flow Conditions ◽  
Ungauged Catchments ◽  
Calibration Process ◽  
Model Efficiency ◽  
Runoff Model ◽  
Calibration Approach

Abstract. Ecologically relevant streamflow characteristics (SFCs) of ungauged catchments are often estimated from simulated runoff of hydrologic models that were originally calibrated on gauged catchments. However, SFC estimates of the gauged donor catchments and subsequently the ungauged catchments can be substantially uncertain when models are calibrated using traditional approaches based on optimization of statistical performance metrics (e.g., Nash–Sutcliffe model efficiency). An improved calibration strategy for gauged catchments is therefore crucial to help reduce the uncertainties of estimated SFCs for ungauged catchments. The aim of this study was to improve SFC estimates from modeled runoff time series in gauged catchments by explicitly including one or several SFCs in the calibration process. Different types of objective functions were defined consisting of the Nash–Sutcliffe model efficiency, single SFCs, or combinations thereof. We calibrated a bucket-type runoff model (HBV – Hydrologiska Byråns Vattenavdelning – model) for 25 catchments in the Tennessee River basin and evaluated the proposed calibration approach on 13 ecologically relevant SFCs representing major flow regime components and different flow conditions. While the model generally tended to underestimate the tested SFCs related to mean and high-flow conditions, SFCs related to low flow were generally overestimated. The highest estimation accuracies were achieved by a SFC-specific model calibration. Estimates of SFCs not included in the calibration process were of similar quality when comparing a multi-SFC calibration approach to a traditional model efficiency calibration. For practical applications, this implies that SFCs should preferably be estimated from targeted runoff model calibration, and modeled estimates need to be carefully interpreted.

On Estimating from a More General Time-Series Cum Cross-Section Data Structure

1987 ◽  
Vol 31 (2) ◽  
pp. 69-71 ◽  
Author(s):  
Badi H. Baltagi
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Cross Section ◽  
Cross Section Data ◽  
Section Data ◽  
General Time

Estimating from a More General Time-Series Cum Cross-Section Data Structure

1976 ◽  
Vol 20 (1) ◽  
pp. 15-21 ◽  
Author(s):  
Sukesh K. Ghosh
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Cross Section ◽  
Cross Section Data ◽  
Section Data ◽  
General Time
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