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TSdeeplearning  

Deep Learning Model for Time Series Forecasting
View on CRAN: Click here


Download and install TSdeeplearning package within the R console
Install from CRAN:
install.packages("TSdeeplearning")

Install from Github:
library("remotes")
install_github("cran/TSdeeplearning")

Install by package version:
library("remotes")
install_version("TSdeeplearning", "0.1.0")



Attach the package and use:
library("TSdeeplearning")
Maintained by
Ronit Jaiswal
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-09-09
Latest Update: 2022-09-09
Description:
RNNs are preferred for sequential data like time series, speech, text, etc. but when dealing with long range dependencies, vanishing gradient problems account for their poor performance. LSTM and GRU are effective solutions which are nothing but RNN networks with the abilities of learning both short-term and long-term dependencies. Their structural makeup enables them to remember information for a long period without any difficulty. LSTM consists of one cell state and three gates, namely, forget gate, input gate and output gate whereas GRU comprises only two gates, namely, reset gate and update gate. This package consists of three different functions for the application of RNN, LSTM and GRU to any time series data for its forecasting. For method details see Jaiswal, R. et al. (2022). <doi:10.1007/s00521-021-06621-3>.
How to cite:
Ronit Jaiswal (2022). TSdeeplearning: Deep Learning Model for Time Series Forecasting. R package version 0.1.0, https://cran.r-project.org/web/packages/TSdeeplearning. Accessed 21 Nov. 2024.
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Complete documentation for TSdeeplearning
Functions, R codes and Examples using the TSdeeplearning R package
Some associated functions: Data_Maize . GRU_ts . LSTM_ts . RNN_ts . 
Some associated R codes: GRU_ts.R . LSTM_ts.R . RNN_ts.R .  Full TSdeeplearning package functions and examples
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