Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network

Ma, Li; Gu, Xiaodu; Wang, Baowei

doi:10.3390/info8020060

Cited by 21 publications

(14 citation statements)

References 33 publications

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“…SWP is a prediction-based approach that utilizes the nearest neighborhood (window) of data. 12,18 In a univariate timeseries X = {x 1 , x 2 , …, x n }, a data point at a particular time instant is reckoned as an outlier if it lies outside the predicted confidence interval (PCI). A single-layer linear network model (an AR-type model) 12,18 is used for prediction with the window as its input.…”

Section: Swp-based Outlier Detection and Correctionmentioning

confidence: 99%

“…The accuracy of SWP being window width dependent, a width that is too small or too large leads to a poor prediction by the single-layer linear network model. 12,18 Unfortunately, an appropriate window width (2k 0 ) calculation procedure is not proposed yet. The correction, solely based on the single-layer linear network model, is not always accurate, as it may not be suitable for all types of nonstationary time-series.…”

Section: Iswp-based Outlier Detection and Correctionmentioning

confidence: 99%

“…Hence, a more robust method is indispensable for outlier detection and correction. On the other hand, the SWP method 12,18,19 is extensively used as it is better able to trace the trends and seasonalities of time‐series, as compared to other algorithms. Unlike, References 8,11 this approach does not consider the statistics of data.…”

Section: Introductionmentioning

confidence: 99%

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An improved sliding window prediction‐based outlier detection and correction for volatile time‐series

Ranjan

Tripathy

Prusty

et al. 2020

Int J Numerical Modelling

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Steady-state forecasting is indispensable for power system planning and operation. A forecasting model for inputs considering their historical record is a preliminary step for such type of studies. Since the historical data quality is decisive in edifice an accurate forecasting model, data preprocessing is essential. Primarily, the quality of raw data is affected by the presence of outliers, and preprocessing refers to outlier detection and correction. In this paper, an effort is made to improve the existing sliding window prediction-based preprocessing method. The recommended reforms are the calculation of appropriate window width and a new outlier correction approach. The proposed method denoted as improved sliding window prediction-based preprocessing is applied to the historical data of PV generation, load power, and the ambient temperature of different time-steps collected from various places in the United States and India. Firstly, the method's efficacy through detailed result analysis demonstrating the proposed preprocessing as a better way than its precursor and k-nearest neighbor approach is presented. Later, the improved out-of-sample forecasting accuracy canonizes the proposed method's concert compared to both the above techniques and the case without preprocessing.

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Section: Swp-based Outlier Detection and Correctionmentioning

confidence: 99%

Section: Iswp-based Outlier Detection and Correctionmentioning

confidence: 99%

See 1 more Smart Citation

An improved sliding window prediction‐based outlier detection and correction for volatile time‐series

Ranjan

Tripathy

Prusty

et al. 2020

Int J Numerical Modelling

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“…The solar radiation incidence attribute values (r_inc) come from the panels mounted on each monitoring station [44,45] and will be exploited for this experiment.…”

Section: Task 1-forecasting Future Data (Istat Dataset)mentioning

confidence: 99%

Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

2018

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This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.

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“…A Smart AgriFood theoretical design is planned in Kaloxylos et al [22], though the creators of [23] present web submission in the agri-nourishment area; Poppe in [24] recommend the investigation to together the extension furthermore the association of ranch generation controls. Garba [25] creates shrewd water-distribution strategies in semi-bone-dry districts; Hlaing et al Present place infections acknowledgment utilizing measurable models; and, in addition, in Alipio et al, there are brilliant hydroponics frameworks with the intention of misuse derivation in Bayesian systems. Marimuthu et al Suggest along with structure a influential knowledge to support brilliant cultivating, whilst likewise abusing recorded timearrangement for creation excellence affirmation [29], on the grounds that these days customers are worried about sustenance wellbeing confirmation identified with wellbeing and prosperity.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications on Agricultural

2019

IJITEE

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The focal point of this investigation is the structure and advancement of viable undertakings, running from yield collect and estimating to absent or wrong sensors information remaking, abusing and contrasting different machine learning procedures with recommend toward which heading to use endeavors and speculations. To deal with a blended data and information originating from genuine datasets that gather a sensor and physical qualities. As beneficial organizations, open or private, huge or little need expanding productivity with costs decrease, finding suitable approaches to misuse information that are ceaselessly recorded and influenced accessible to can be the correct decision to accomplish these objectives. Agrarian field is just clearly obstinate to the advanced innovation and the "shrewd homestead" demonstrate is progressively across the board by misusing the Internet of Things (IoT) worldview connected toward ecological moreover verifiable data from side to side timearrangement. The outcomes demonstrate how there are sufficient edges for development while supporting solicitations and necessities originating from organizations that desire to utilize a practical and enhanced horticulture mechanical business, putting in innovation, as well as in the information also in talented labor force compulsory to remove the greatest from it.

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Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network

Cited by 21 publications

References 33 publications

An improved sliding window prediction‐based outlier detection and correction for volatile time‐series

An improved sliding window prediction‐based outlier detection and correction for volatile time‐series

Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

Machine Learning Applications on Agricultural

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