Recurrent Neural Networks for Meteorological Time Series Imputation

Flores, Anibal; Tito, Hugo; Centty, Deymor

doi:10.14569/ijacsa.2020.0110360

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…• RNN: The outcome from the prior step is provided as input to the ongoing step in an RNN, a form of neural network [22]. In contrast to conventional neural networks, RNN input and output are interdependent.…”

Section: E Model Developmentmentioning

confidence: 99%

Aspect-based Sentiment Analysis for Bengali Text using Bidirectional Encoder Representations from Transformers (BERT)

Samia¹,

Rajee²,

Hasan³

et al. 2022

IJACSA

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Public opinion is important for decision-making on numerous occasions for national growth in democratic countries like Bangladesh, the USA, and India. Sentiment analysis is a technique used to determine the polarity of opinions expressed in a text. The more complex stage of sentiment analysis is known as Aspect-Based Sentiment Analysis (ABSA), where it is possible to ascertain both the actual topics being discussed by the speakers as well as the polarity of each opinion. Nowadays, people leave comments on a variety of websites, including social networking sites, online news sources, and even YouTube video comment sections, on a wide range of topics. ABSA can play a significant role in utilizing these comments for a variety of objectives, including academic, commercial, and socioeconomic development. In English and many other popular European languages, there are many datasets for ABSA, but the Bengali language has very few of them. As a result, ABSA research on Bengali is relatively rare. In this paper, we present a Bengali dataset that has been manually annotated with five aspects and their corresponding sentiment. A baseline evaluation was also carried out using the Bidirectional Encoder Representations from Transformers (BERT) model, with 97% aspect detection accuracy and 77% sentiment classification accuracy. For aspect detection, the F1-score was 0.97 and for sentiment classification, it was 0.77.

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Section: E Model Developmentmentioning

confidence: 99%

Aspect-based Sentiment Analysis for Bengali Text using Bidirectional Encoder Representations from Transformers (BERT)

Samia¹,

Rajee²,

Hasan³

et al. 2022

IJACSA

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“…Moreover, since univariate time series have no other correlated variables except time, these algorithms fail for missing data imputation of univariate time series [4,9]. Different methods are used for the infilling or reconstruction of missing data, including: univariate time series [9,[14][15][16][17][18], hydroclimatic values, and streamflow values [7,[19][20][21][22][23][24][25][26][27], among others. However, some drawbacks from some of these studies have been identified.…”

Section: Introductionmentioning

confidence: 99%

“…However, some drawbacks from some of these studies have been identified. Some of these studies (especially for univariate time series) focus on the introduction of somewhat novel complex algorithms, which are supposed to perform satisfactorily across board for all univariate time series without much attention to the fact that different data possess different attributes (see [17,18,[28][29][30][31][32][33]).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Missing Data Infilling Mechanisms for Recovering a Real-World Single Station Streamflow Observation

Baddoo

Odai

et al. 2021

IJERPH

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Reconstructing missing streamflow data can be challenging when additional data are not available, and missing data imputation of real-world datasets to investigate how to ascertain the accuracy of imputation algorithms for these datasets are lacking. This study investigated the necessary complexity of missing data reconstruction schemes to obtain the relevant results for a real-world single station streamflow observation to facilitate its further use. This investigation was implemented by applying different missing data mechanisms spanning from univariate algorithms to multiple imputation methods accustomed to multivariate data taking time as an explicit variable. The performance accuracy of these schemes was assessed using the total error measurement (TEM) and a recommended localized error measurement (LEM) in this study. The results show that univariate missing value algorithms, which are specially developed to handle univariate time series, provide satisfactory results, but the ones which provide the best results are usually time and computationally intensive. Also, multiple imputation algorithms which consider the surrounding observed values and/or which can understand the characteristics of the data provide similar results to the univariate missing data algorithms and, in some cases, perform better without the added time and computational downsides when time is taken as an explicit variable. Furthermore, the LEM would be especially useful when the missing data are in specific portions of the dataset or where very large gaps of ‘missingness’ occur. Finally, proper handling of missing values of real-world hydroclimatic datasets depends on imputing and extensive study of the particular dataset to be imputed.

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Recurrent Neural Networks for Meteorological Time Series Imputation

Cited by 2 publications

References 9 publications

Aspect-based Sentiment Analysis for Bengali Text using Bidirectional Encoder Representations from Transformers (BERT)

Aspect-based Sentiment Analysis for Bengali Text using Bidirectional Encoder Representations from Transformers (BERT)

Comparison of Missing Data Infilling Mechanisms for Recovering a Real-World Single Station Streamflow Observation

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