Univariate Time Series Data Forecasting of Air Pollution using LSTM Neural Network

Hamami, Faqih; Dahlan, Iqbal Ahmad

doi:10.1109/icadeis49811.2020.9277393

Cited by 25 publications

(8 citation statements)

References 4 publications

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“…The results show that the three best methods are random forest, Gradient Boosting, and K-Nearest Neighbours with precision, recall, and f1-score values more than 0.63. Another research is implemented by using deep learning with LSTM algorithm [4]. This paper developed LSTM network for air pollution concentrations forecasting and reveal that RMSE around at 5.58%.…”

Section: Methodsmentioning

confidence: 99%

Air Quality Classification in Urban Environment using Machine Learning Approach

Hamami

Dahlan

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Air pollution comes from human activities that can threaten living things. It is affected by gasses including PM10, SO2, CO, O3, NO2 and others. Air pollution leads to dangerous diseases even death. Monitoring air quality is important task to understand pollution concentration. Air quality monitoring is better when it can classify whether air quality is habitable or not. This research proposes air quality classification using classification algorithms such as Logistic Regression, KKN, Decision Tree, and Random Forest algorithm. Dataset was taken from Jakarta’s open data for 12 months with several attributes including gas concentration and with several pre-processing steps. Based on experiment, decision tree model has the best accuracy to classify air quality level up to 100% with tuning several hyperparameters.

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Section: Methodsmentioning

confidence: 99%

Air Quality Classification in Urban Environment using Machine Learning Approach

Hamami

Dahlan

2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…Li et al [26] Wireless Communications and Mobile Computing algorithm is used to predict electricity prices, and the results show good prediction results. However, the LSTM algorithm was attempted to be applied to time series problems because of the difficulty of modeling future situations using timeseries features in traditional neural networks [27]. The LSTM algorithm can avoid gradient disappearance and also can solve long-term problems.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning with Multisource Data Fusion in Electricity Internet of Things for Electricity Price Forecast

Xie

Luo

et al. 2022

Wireless Communications and Mobile Computing

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More and more IoT (Internet of Thing) devices have been connected to our lives in recent years, making life more convenient. Many countries are also making use of Internet of Thing technology to carry out intelligent electricity network reform. One of the reform goals is balancing the supply and demand of electricity, which has become a top priority. Balancing electricity supply and demand through real-time electricity prices has become an effective way. However, using traditional machine learning models for real-time electricity price prediction requires complex feature engineering, and the results are not satisfactory. Also, the mainstream fusion methods use data-level fusion, which will put very high pressure on communication bandwidth and computer resources. In this paper, an LSTM- (long short-term memory-) based decision level fusion of multisource data is proposed and applied for real-time electricity price prediction on actual electricity price datasets. The method solves the difficulties of traditional machine learning models in dealing with complex nonlinear problems. It achieves local asynchronous processing of multisource data through decision-level fusion, reducing the requirement for bandwidth resources and providing perfect results in real-time electricity price prediction. The experimental results show that the prediction accuracy of the decision fusion prediction model based on LSTM is higher than that of the linear regression algorithm.

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“…We propose that fully understanding the meaning of these data will often require complexity scientists to model them as time series. Examples include data collected by sensors (CRS, 2020;Evans, 2011), every day natural language (e.g., Bentley et al, 2018;Ross et al, 2020;Reagan et al, 2016;Chu et al, 2017), biomonitors (Gharehbaghi and Lindén, 2018), waterflow, barometric pressure and other routine environmental condition meters (e.g., Hamami and Dahlan, 2020;Javed et al, 2020a;Ewen, 2011), social media interactions (e.g., De Bie et al, 2016;Javed and Lee, 2018, and hourly financial data reported by fluctuating world stock and currency markets (Lasfer et al, 2013). In response to the increasing amounts of time-oriented data available to analysts, the applications of time-series modeling are growing rapidly (e.g., Minaudo et al, 2017;Dupas et al, 2015;Mather and Johnson, 2015;Bende-Michl et al, 2013;Iorio et al, 2018;Gupta and Chatterjee, 2018;Pirim et al, 2012;Souto et al, 2008;Flanagan et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations

Ali¹,

Rizzo²,

Lee³

et al. 2021

Preprint

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There is an increasing demand for scalable algorithms capable of clustering and analyzing large time series datasets. The Kohonen self-organizing map (SOM) is a type of unsupervised artificial neural network for visualizing and clustering complex data, reducing the dimensionality of data, and selecting influential features. Like all clustering methods, the SOM requires a measure of similarity between input data (in this work time series). Dynamic time warping (DTW) is one such measure, and a top performer given that it accommodates the distortions when aligning time series. Despite its use in clustering, DTW is limited in practice because it is quadratic in runtime complexity with the length of the time series data.To address this, we present a new DTW-based clustering method, called SOMTimeS (a Self-Organizing Map for TIME Series), that scales better and runs faster than other DTW-based clustering algorithms, and has similar performance accuracy. The computational performance of SOMTimeS stems from its ability to prune unnecessary DTW computations during the SOM's training phase. We also implemented a similar pruning strategy for K-means for comparison with one of the top performing clustering algorithms. We evaluated the pruning effectiveness, accuracy, execution time and scalability on 112 benchmark time series datasets from the University of California, Riverside classification archive. We showed that for similar accuracy, the speed-up achieved for SOMTimeS and K-means was 1.8x on average; however, rates varied between 1x and 18x depending on the dataset. SOMTimeS and K-means pruned

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Univariate Time Series Data Forecasting of Air Pollution using LSTM Neural Network

Cited by 25 publications

References 4 publications

Air Quality Classification in Urban Environment using Machine Learning Approach

Air Quality Classification in Urban Environment using Machine Learning Approach

Deep Learning with Multisource Data Fusion in Electricity Internet of Things for Electricity Price Forecast

SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations

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