No packages match

AutoNN - Automatic Neural Network Modeling for Time Series Forecasting

Provides optimal combinations of input nodes and hidden neurons for fitting feedforward single-layer artificial neural networks in time series forecasting. Models are evaluated using root mean square error, mean absolute percentage error, and mean absolute error measures.

Last updated

1.00 score

AutoNN - Automatic Neural Network Modeling for Time Series Forecasting

Provides optimal combinations of input nodes and hidden neurons for fitting feedforward single-layer artificial neural networks in time series forecasting. Models are evaluated using root mean square error, mean absolute percentage error, and mean absolute error measures.

Last updated

1.00 score

CDVI - Cuddy-Della Valle Index for Capturing the Instability in Time Series Data

Cuddy-Della valle index gives the degree of instability present in the data by accommodating the effect of a trend. The adjusted R squared value of the best fitted model is chosen. The index is obtained by multiplying the coefficient of variation with square root of one minus the adjusted R-squared value. This package has been developed using concept of Shankar et al. (2022)<doi:10.3389/fsufs.2023.1208898>.

Last updated

1.00 score 1 scripts 193 downloads

CGR - Compound Growth Rate for Capturing the Growth Rate Over the Period

The compound growth rate indicates the percentage change of a specific variable over a defined period. It is calculated using non-linear models, particularly the exponential model. To estimate the compound growth rates, the growth model is first converted to semilog form and then analyzed using Ordinary Least Squares (OLS) regression. This package has been developed using concept of Shankar et al. (2022)<doi:10.3389/fsufs.2023.1208898>.

Last updated

1.00 score 1 scripts 205 downloads