tsmodeleval

The function `<code>tsmodeleval</code>` can be used to evaluate the goodness of fit of a time series model.

Fundamental time series forecasting models such as autoregressive integrated moving average (ARIMA), exponential smoothing, and simple moving average are included. For ARIMA models, the output follows the traditional parameterisation by Box and Jenkins (1970, ISBN: 0816210942, 9780816210947). Furthermore, there are functions for detailed time series exploration and decomposition, respectively. All data and result visualisations are generated by 'ggplot2' instead of conventional R graphical output. For more details regarding the theoretical background of the models see Hyndman, R.J. and Athanasopoulos, G. (2021) <https://otexts.com/fpp3/>.

Ka Karl Wu

tsforecast

Time Series Forecasting Functions

Ka Yui Karl Wu

tsmodeleval function

<dl><dt>object</dt>
<dd>a time series model of class `<code>tsarima</code>`, `<code>tsesm</code>`, or `<code>tsmovav</code>`. It can also be a list with at least the two elements: `<code>x</code>` and `<code>fitted</code>`.</dd></dl>

Arguments

Author

Goodness of Fit of a Time Series Model — tsmodeleval

<dl>

<dt>object</dt>
<dd>a time series model of class `<code>tsarima</code>`, `<code>tsesm</code>`, or `<code>tsmovav</code>`. It can also be a list with at least the two elements: `<code>x</code>` and `<code>fitted</code>`.</dd>

</dl>

tsmodeleval: Goodness of Fit of a Time Series Model

Description

Usage

Value

Arguments

Author

References

Examples