Using Hampel Identifier to Eliminate Profile-Isolated Outliers in Laser Vision Measurement

Yao, Zhibin; Xie, Jiaquan; Tian, Yaqin; Huang, Qingxue

doi:10.1155/2019/3823691

Cited by 19 publications

(6 citation statements)

References 24 publications

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“…Outlier detection and treatment is a vital step in enhancing data quality. We use the Hampel filter which is based on the Hampel identifier (Yao et al, 2019 ). The Hampel identifier is a reliable and efficient outlier detection technique that has been used in a variety of fields.…”

Section: Methodology and Datamentioning

confidence: 99%

Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks

Tripathi

Sharma

2022

Comput Econ

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Bitcoin is a volatile financial asset that runs on a decentralized peer-to-peer Blockchain network. Investors need accurate price forecasts to minimize losses and maximize profits. Extreme volatility, speculative nature, and dependence on intrinsic and external factors make Bitcoin price forecast challenging. This research proposes a reliable forecasting framework by reducing the inherent noise in Bitcoin time series and by examining the predictive power of three distinct types of predictors, namely fundamental indicators, technical indicators, and univariate lagged prices. We begin with a three-step hybrid feature selection procedure to identify the variables with the highest predictive ability, then use Hampel and Savitzky–Golay filters to impute outliers and remove signal noise from the Bitcoin time series. Next, we use several deep neural networks tuned by Bayesian Optimization to forecast short-term prices for the next day, three days, five days, and seven days ahead intervals. We found that the Deep Artificial Neural Network model created using technical indicators as input data outperformed other benchmark models like Long Short Term Memory, Bi-directional LSTM (BiLSTM), and Convolutional Neural Network (CNN)-BiLSTM. The presented results record a high accuracy and outperform all existing models available in the past literature with an absolute percentage error as low as 0.28% for the next day forecast and 2.25% for the seventh day for the latest out of sample period ranging from Jan 1, 2021, to Nov 1, 2021. With contributions in feature selection, data-preprocessing, and hybridizing deep learning models, this work contributes to researchers and traders in fundamental and technical domains.

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Section: Methodology and Datamentioning

confidence: 99%

Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks

Tripathi

Sharma

2022

Comput Econ

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“…The threshold is defined as a constant (threshold factor) multiplied by the median absolute deviation. An asymmetric window with a bandwidth ratio of 0.004 (0.4 %) and a threshold factor of 3 was adopted following previous studies (Davies and Gather, 1993;Pearson, 2002;Liu et al, 2004;Yao et al, 2019). The data identified as outliers were then removed from the dataset.…”

Section: Outlier Detectionmentioning

confidence: 99%

Evaluation of filtering methods for use on high-frequency measurements of landslide displacements

Sharifi

Hendry

Macciotta

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. Displacement monitoring is a critical control for risks associated with potentially sudden slope failures. Instrument measurements are, however, obscured by the presence of scatter. Data filtering methods aim to reduce the scatter and therefore enhance the performance of early warning systems (EWSs). The effectiveness of EWSs depends on the lag time between the onset of acceleration and its detection by the monitoring system such that a timely warning is issued for the implementation of consequence mitigation strategies. This paper evaluates the performance of three filtering methods (simple moving average, Gaussian-weighted moving average, and Savitzky–Golay) and considers their comparative advantages and disadvantages. The evaluation utilized six levels of randomly generated scatter on synthetic data, as well as high-frequency global navigation satellite system (GNSS) displacement measurements at the Ten-mile landslide in British Columbia, Canada. The simple moving average method exhibited significant disadvantages compared to the Gaussian-weighted moving average and Savitzky–Golay approaches. This paper presents a framework to evaluate the adequacy of different algorithms for minimizing monitoring data scatter.

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“…Hampel indetifier (HI) is an effective outlier identification and correction technique, which utilizes the median deviation and absolute median deviation as the criteria for determining the outliers, with good robustness (Yao et al 2019) (2) Where 𝑘 = (1/(√2erfc −1 (1/2))) ≈ 1.4826 is the unbiased estimate of the Gaussian distribution.…”

Section: Hampel Indetifiermentioning

confidence: 99%

Multi-step-ahead and interval carbon price forecasting using transformer-based hybrid model

Wang

Zhong

Wang

et al. 2022

Preprint

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Accurate and stable carbon price forecasts provide a reference for the stability of the carbon market and significantly improve investment and operational decisions. However, due to the non-linear and non-stationarity characteristics of carbon price series and its complex fluctuation features, realizing this goal is still a significant challenge, and researchers usually ignore multi-step and interval forecasting. To accurately predict the carbon price, a novel hybrid multi-step and interval carbon price forecasting model is proposed in this study, based on Hampel identifier(HI), time-varying filtering-based empirical mode decomposition (TVFEMD), and Transformer. Firstly, HI identifies and corrects outliers in carbon price. Then, carbon price is decomposed by TVFEMD into several intrinsic mode functions(imfs) to reduce the non-linear and non-stationarity of carbon price, to obtain more regular features in series, and these imfs are reconstructed by sample entropy(SE). Subsequently, the Orthogonal Array Tuning Method(OATM) is used to optimize the Transformer's hyperparameters to obtain the optimal model structure. Finally, The Transformer after hyperparameter optimization and quantile loss function is used to perform multi-step and interval forecasting on each part of the reconstruction, and the final prediction result is obtained by summing them up. Five pilot carbon trading markets in China were selected as experimental objects to verify the proposed model's prediction performance, and a variety of benchmark models and evaluation indicators were selected for comparison and analysis. The experimental results show that the proposed hybrid model is superior to the benchmark model in most aspects, and the interval forecast can well describe the uncertainty of carbon price fluctuations. Therefore, the proposed hybrid model is a reliable carbon price forecasting tool that can provide a reliable reference for policymakers and investors.

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Using Hampel Identifier to Eliminate Profile-Isolated Outliers in Laser Vision Measurement

Cited by 19 publications

References 24 publications

Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks

Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks

Evaluation of filtering methods for use on high-frequency measurements of landslide displacements

Multi-step-ahead and interval carbon price forecasting using transformer-based hybrid model

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