An improved sliding window <scp>prediction‐based</scp> outlier detection and correction for volatile <scp>time‐series</scp>

Ranjan, Kumar Gaurav; Tripathy, Debesh Shankar; Prusty, B Rajanarayan; Jena, Debashisha

doi:10.1002/jnm.2816

Cited by 39 publications

(15 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The detection and the appropriate correction of outliers are accomplished via the improved sliding window prediction-based method. [43][44][45] Since QR, QRF, and QkNN 20,21 The optimal value of k is searched from a grid of values from 20 to 360 at a step of 20 by minimizing (4) for the QkNNRA and QkNN models and is presented in Table 1. The analyses of results are done to assess the forecast performance of the proposed QkNNRA model in terms of the quantile forecasts and further the construction of PIs.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

A k‐nearest neighbor‐based averaging model for probabilistic PV generation forecasting

Tripathy

Prusty

Bingi

2022

Int J Numerical Modelling

Self Cite

View full text Add to dashboard Cite

Probabilistic forecasting of PV generation is crucial in uncertainty management to reinforce PV-integrated power systems for long-term planning. In this context, developing a reliable probabilistic forecast model is challenging due to weather conditions' stochastic nature and varying daily PV production patterns at multiple time instants. Due to varying probability distribution patterns, a nonparametric approach, such as quantile regression, is challenging to approximate the forecast error distribution. A forecast combination concept that yields impressive probabilistic forecasting results has rarely been utilized for PV generation forecasting. The use of complementary and sensible point forecasters characterizing different aspects of intricate PV production patterns in a k-nearest neighbor quantile regression averaging framework is the proposed probabilistic forecasting model. The proposed forecasting model's accuracy is assessed with real-world PV generation data from the USA for multi-time instants. The probabilistic forecast performance of the proposed quantile k-nearest neighbors regression averaging model is evaluated against the traditional quantile regression averaging, quantile regression forests, quantile k-nearest neighbors, basic quantile regression, and regression bootstrapping in terms of the forecast (quantiles and prediction intervals) accuracy, using relevant error measures and scores. The proposed model has superior performance compared to the other models, as indicated by the lowest forecast errors and scores.autoregressive conditional heteroscedastic (ARCH), k-nearest neighbors (kNN), multiple linear regression (MLR), probabilistic PV generation forecasting, quantile regression averaging (QRA) | INTRODUCTIONIn recent days, research and development in PV technology have risen multi-folds making it more efficient, cost-effective, and easy to commission, leading toward a viable alternative to fossil fuels. Integrating PV generations into power systems requires tackling many challenges in planning, operation, and maintenance. 1,2 The variable PV power

show abstract

Section: Discussion Of Resultsmentioning

confidence: 99%

A k‐nearest neighbor‐based averaging model for probabilistic PV generation forecasting

Tripathy

Prusty

Bingi

2022

Int J Numerical Modelling

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, time sliding window has many applications in time series prediction 40 . Ranjan et al 41 proposed an outlier detection model based on time sliding window to predict the steady state of power system. Gaur et al 42 proposed two sliding window techniques to enhance binary classification of moving images.…”

Section: Related Workmentioning

confidence: 99%

A long short‐term memory‐based model for greenhouse climate prediction

et al. 2021

View full text Add to dashboard Cite

Greenhouses can grow many off‐season vegetables and fruits, which improves people's quality of life. Greenhouses can also help crops resist natural disasters and ensure the stable growth of crops. However, it is highly challenging to carefully control the greenhouse climate. Therefore, the proposal of a greenhouse climate prediction model provides a way to solve this challenge. We focus on the six climatic factors that affect crops growth, including temperature, humidity, illumination, carbon dioxide concentration, soil temperature and soil humidity, and propose a GCP_lstm model for greenhouse climate prediction. The climate change in greenhouse is nonlinear, so we use long short‐term memory (LSTM) model to capture the dependence between historical climate data. Moreover, the short‐term climate has a greater impact on the future trend of greenhouse climate change. Therefore, we added a 5‐min time sliding window through the analysis experiment. In addition, sensors sometimes collect wrong climate data. Based on the existence of abnormal data, our model still has good robustness. We experienced our method on the data sets of three vegetables: tomato, cucumber and pepper. The comparison shows that our method is better than other comparison models.

show abstract

“…After adding attention mechanism, the performance of the model has been improved. Furthermore, the time sliding window 34 can make the model better find the data information in a certain period of time. Therefore, we use time sliding window and attention mechanism to improve the performance of the model.…”

Section: Related Workmentioning

confidence: 99%

Bidirectional GRU networks‐based next POI category prediction for healthcare

Liu

Song

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

The Corona Virus Disease 2019 has a great impact on public health and public psychology. People stay at home for a long time and rarely go out. With the improvement of the epidemic situation, people began to go to different places to check in. To maintain public mental health, it is necessary to propose a point‐of‐interest (POI) prediction model which can mine users' interests. However, the current techniques suffer from lower precision during prediction and the practical value is poor, which is due to the sparse data of users' check‐in. Faced with this challenge, we propose an attention‐based bidirectional gated recurrent unit (GRU) model for POI category prediction (ABG_poic). We regard the user's POI category as the user's interest preference because the fuzzy POI category is easier to reflect the user's interest than the POI. This method can alleviate the data sparsity, and protect users' location privacy. Since users' preferences are variable, we utilize a bidirectional GRU to capture the dynamic dependence of users' check‐ins. Furthermore, since the neural network is similar to a “black box” in feature learning, the decision‐making stage is opaque. Thus, we combine the attention mechanism with bidirectional GRU to selectively focus on historical check‐in records, which can improve the interpretability of the model. Considering the time impact on users' check‐in, we utilize the time sliding window in the ABG_poic model. Experiments on two data sets demonstrate that our ABG_poic outperforms the comparison models for POI category prediction on sparse check‐in data.

show abstract

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

Cited by 39 publications

References 23 publications

A k‐nearest neighbor‐based averaging model for probabilistic PV generation forecasting

A k‐nearest neighbor‐based averaging model for probabilistic PV generation forecasting

A long short‐term memory‐based model for greenhouse climate prediction

Bidirectional GRU networks‐based next POI category prediction for healthcare

Contact Info

Product

Resources

About