sigmoid

The sigmoid function is a smooth, S-shaped function that maps any real-valued input
into a bounded interval, typically (0,1) . It is widely used in probability modeling,
logistic regression, and neural networks as an activation function.

A suite of auxiliary functions that enhance time series estimation and forecasting, including a robust anomaly detection routine based on Chen and Liu (1993) <doi:10.2307/2290724> (imported and wrapped from the 'tsoutliers' package), utilities for managing calendar and time conversions, performance metrics to assess both point forecasts and distributional predictions, advanced simulation by allowing the generation of time series components—such as trend, seasonal, ARMA, irregular, and anomalies—in a modular fashion based on the innovations form of the state space model and a number of transformation methods including Box-Cox, Logit, 'Softplus-Logit' and Sigmoid.

Alexios Ghalanos

tsaux

Time Series Forecasting Auxiliary Functions

Alexios Galanos

sigmoid function

<dl><dt>lower</dt>
<dd>lower bound of the variable.</dd>
<dt>upper</dt>
<dd>upper bound of the variable.</dd>
<dt>...</dt>
<dd>not currently used.</dd></dl>

Arguments

Author

The sigmoid transformation — sigmoid

<dl>

<dt>lower</dt>
<dd>lower bound of the variable.</dd>


<dt>upper</dt>
<dd>upper bound of the variable.</dd>


<dt>...</dt>
<dd>not currently used.</dd>

</dl>

sigmoid: The sigmoid transformation

Description

Usage

Value

Arguments

Author

Examples