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Time Series Analysis- A Comparative Analysis Between ANN and RNN

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dc.contributor.author Rafi, Taki Hasan
dc.contributor.author Rinky, Rehnuma Karim
dc.date.accessioned 2020-09-09T09:31:50Z
dc.date.available 2020-09-09T09:31:50Z
dc.date.issued 2020-07
dc.identifier.issn 1818-5878
dc.identifier.issn 2408-8498
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/4233
dc.description.abstract Time series analysis is a significant undertaking in time series data mining and has pulled in extraordinary interests and huge endeavours during the most recent decades. However, data handling is a prior task in time series data. The main objective of time series analysis is to get intuition of the data. In recent years, AI models come with some enormous results in time series analysis. The motivation of this study is to determine a suitable artificial intelligence-based model for time-series related analysis. In this comparative analysis, authors utilize a publically accessible dataset to conduct the research. Author utilizes Long Short Term Memory (LSTM) model along with Feed Forward Neural Network (FNN), Time lagged Neural Network (TLNN) and Seasonal Artificial Neural Network (SANN) to compare the performance. However, some extensively used performance matrices such as Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) utilizes the evaluate all the models. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Time-series analysis en_US
dc.subject Neural networks en_US
dc.title Time Series Analysis- A Comparative Analysis Between ANN and RNN en_US
dc.type Article en_US

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