An algorithm for outlier detection in a time series model using backpropagation neural network

Vishwakarma, Gajendra K.; Paul, Chinmoy; Elsawah, A. M.

doi:10.1016/j.jksus.2020.09.018

Cited by 22 publications

(6 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 9 shows actual, fitted,and forecasted values; it can be seen how the model captures the behavior and movements of actual series, reaching higher and lower values over the years. It is important to note that the series shown in Figure 9 are values after cleaning series and do not consider extreme value shown in Figure 3a, which is an atypical value that was not considered due to being a value whose behavior could bias or influence over model selection and parameters estimation [66,67].…”

Section: Forecastingmentioning

confidence: 99%

Stochastic Approaches Systems to Predictive and Modeling Chilean Wildfires

de la Fuente-Mella,

Elórtegui-Gómez,

Umaña-Hermosilla

et al. 2023

Mathematics

View full text Add to dashboard Cite

Whether due to natural causes or human carelessness, forest fires have the power to cause devastating damage, alter the habitat of animals and endemic species, generate insecurity in the population, and even affect human settlements with significant economic losses. These natural and social disasters are very difficult to control, and despite the multidisciplinary human effort, it has not been possible to create efficient mechanisms to mitigate the effects, and they have become the nightmare of every summer season. This study focuses on forecast models for fire measurements using time-series data from the Chilean Ministry of Agriculture. Specifically, this study proposes a comprehensive methodology of deterministic and stochastic time series to forecast the fire measures required by the programs of the National Forestry Corporation (CONAF). The models used in this research are among those commonly applied for time-series data. For the number of fires series, an Autoregressive Integrated Moving Average (ARIMA) model is selected, while for the affected surface series, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model is selected, in both cases due to the lowest error metrics among the models fitted. The results provide evidence on the forecast for the number of national fires and affected national surface measured by a series of hectares (ha). For the deterministic method, the best model to predict the number of fires and affected surface is double exponential smoothing with damped parameter; for the stochastic approach, the best model for forecasting the number of fires is an ARIMA (2,1,2); and for affected surface, a SARIMA(1,1,0)(2,0,1)4, forecasting results are determined both with stochastic models due to showing a better performance in terms of error metrics.

show abstract

Section: Forecastingmentioning

confidence: 99%

Stochastic Approaches Systems to Predictive and Modeling Chilean Wildfires

de la Fuente-Mella,

Elórtegui-Gómez,

Umaña-Hermosilla

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…The backpropagation network in the training process has three stages (Vishwakarma et al, 2020), namely:…”

Section: Resilient Backpropagation Neural Networkmentioning

confidence: 99%

K-Means – Resilient Backpropagation Neural Network in Predicting Poverty Levels

Poerwanto

2023

View full text Add to dashboard Cite

In solving economic problems, the government has implemented several development policies. However, this policy is considered to be too centered on big cities. So, through this research it is hoped that it can provide an overview related to regional groups that fall into the poorer category so that the government can also provide accelerated development policies that are oriented towards improving the economy of residents in the area. This study aims to determine the results of classifying district/city poverty levels in Indonesia as a basis for classification for predictions and to classify district/city poverty levels based on influencing factors. The method used in this study is K-Means Clustering using the poverty depth index and poverty severity index variables, then proceed with using the Backpropagation Neural Network (BNN) algorithm using the GRDP, per capita expenditure, human development index, and mean years of schooling. The results obtained using the K-Means algorithm are that there are 42 districts/cities that belong to cluster 1 where this region has a poverty index depth and severity index value that is higher than the 472 districts/cities in cluster 2. Furthermore, the cluster results are used as response variables for classification with BNN. The accuracy of the model obtained is very high, which is equal to 98.06, so the model is very feasible to be used as a poverty rate prediction model based on the variables used.

show abstract

“…These solutions imply using different heuristics rules or machine learning algorithms. From the perspective of the learning models, there are three main categories: unsupervised [9,10], supervised [11,12], and semi-supervised anomaly detection [13,14]. As we apply unsupervised methods, our focus in this section is going to be only on these techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Vishwakarma et al [10] present another deep learning unsupervised approach. The proposed solution uses Feed Forward neural networks, and it is limited to univariate time series.…”

Section: Related Workmentioning

confidence: 99%

Change Point Enhanced Anomaly Detection for IoT Time Series Data

Apostol

Truică

Pop

et al. 2021

Water

View full text Add to dashboard Cite

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

show abstract

An algorithm for outlier detection in a time series model using backpropagation neural network

Cited by 22 publications

References 30 publications

Stochastic Approaches Systems to Predictive and Modeling Chilean Wildfires

Stochastic Approaches Systems to Predictive and Modeling Chilean Wildfires

K-Means – Resilient Backpropagation Neural Network in Predicting Poverty Levels

Change Point Enhanced Anomaly Detection for IoT Time Series Data

Contact Info

Product

Resources

About