Exponential smoothing for irregular data

Cipra, Tomáš

doi:10.1007/s10492-006-0023-9

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…Combined, these challenges result in an irregularly sampled first and second order non-stationary time-series not suitable for analysis with standard exponential smoothing techniques. An alternative is damped double exponential smoothing for irregular data [Cipra 2006]. The approach smooths both a damped trend and the component not explained by the trend.…”

Section: Outlier Detection Time-series Modelmentioning

confidence: 99%

“…Combined with Wright's method for irregularly spaced data, a smoothing algorithm can be derived that is applicable to groundwater hydrographs. From Cipra [2006], the smoothed estimate at time t n , , is estimated from the weighted sum of the current observation, (i.e. the observed groundwater level), the smoothed estimate from the prior time step, , and the damped trend, :…”

Section: Outlier Detection Time-series Modelmentioning

confidence: 99%

“…Departing from Cipra [2006], the initial values for and were estimated using a smoothing spline (see Matlab csaps function) fit to the entire observation record, from which the initial spline head and slope are extracted. Additionally, inclusion of the damping coefficient (0 < φ < 1) was found to add little benefit at the cost of increased calibration demand.…”

Section: Outlier Detection Time-series Modelmentioning

confidence: 99%

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The good, the bad and the outliers: automated detection of errors and outliers from groundwater hydrographs

Peterson

Western

Chen³

2017

Hydrogeol J

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Suspicious groundwater-level observations are common and can arise for many reasons ranging from an unforeseen biophysical process to bore failure and data management errors. Unforeseen observations may provide valuable insights that challenge existing expectations and can be deemed outliers, while monitoring and data handling failures can be deemed errors, and, if ignored, may compromise trend analysis and groundwater model calibration. Ideally, outliers and errors should be identified but to date this has been a subjective process that is not reproducible and is inefficient. This paper presents an approach to objectively and efficiently identify multiple types of errors and outliers.The approach requires only the observed groundwater hydrograph, requires no particular consideration of the hydrogeology, the drivers (e.g. pumping) or the monitoring frequency, and is freely available in the HydroSight toolbox. Herein, the algorithms and time-series model are detailed and applied to four observation bores with varying dynamics. The detection of outliers was most reliable when the observation data were acquired quarterly or more frequently. Outlier detection where the groundwater-level variance is non-stationary or the absolute trend increases rapidly was more challenging, with the former likely to result in an under-estimation of the number of outliers and the latter an over-estimation in the number of outliers.

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Section: Outlier Detection Time-series Modelmentioning

confidence: 99%

Section: Outlier Detection Time-series Modelmentioning

confidence: 99%

Section: Outlier Detection Time-series Modelmentioning

confidence: 99%

See 1 more Smart Citation

The good, the bad and the outliers: automated detection of errors and outliers from groundwater hydrographs

Peterson

Western

Chen³

2017

Hydrogeol J

View full text Add to dashboard Cite

show abstract

“…In the following year, Velicer and Colby [7] compared four different data-missing methods (the deletion, mean substitution, mean of adjacent observations and maximum likelihood estimation methods) commonly used for autoregressive integrated moving average (ARIMA) models time series under 50 different data-missing conditions, their results showing that the maximum likelihood estimation method performed the best under all conditions tested. In 2006, Cipra and Praha [8] made a modification to the double smoothing method that was similar to that Wright made to Holt's method and suggested that the modified double smoothing method performed better than the modified Holt method. However, this comparison may not be fair, because Cipra and Praha did not optimize the coefficients in these methods.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Traffic Speed Forecasting Based on Data Recorded at Irregular Intervals

Wong

Szeto

2012

IEEE Trans. Intell. Transport. Syst.

105

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As demand for proactive real-time transportation management systems has grown, major developments have been seen in short-time traffic forecasting methods. Recent studies have introduced time series theory, neural networks, genetic algorithms, etc., to short-time traffic forecasting to make forecasts more reliable, efficient and accurate. However, most of these methods can only deal with data recorded at regular time intervals, thereby restricting the range of data collection tools to loop detectors or other equipment that generate regular data. The study reported here represents an attempt to expand on several existing time series forecasting methods to accommodate data recorded at irregular time intervals, thus ensuring these methods can be used to obtain predicted traffic speeds through intermittent data sources such as the GPS. The study tested several methods using the GPS data from 480 Hong Kong taxis. The results show that the best performance is obtained using a neural network model with acceleration information predicted by ARIMA model.

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Financial Time Series Prediction in Cooperating with Event Knowledge: A Fuzzy Approach

Sang

Woo

Park

et al. 2010

Advances in Soft Computing

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Exponential smoothing for irregular data

Cited by 10 publications

References 9 publications

The good, the bad and the outliers: automated detection of errors and outliers from groundwater hydrographs

The good, the bad and the outliers: automated detection of errors and outliers from groundwater hydrographs

Short-Term Traffic Speed Forecasting Based on Data Recorded at Irregular Intervals

Financial Time Series Prediction in Cooperating with Event Knowledge: A Fuzzy Approach

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