accuracy_snaive

Calculate accuracy measure based on snaive method

A novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details see our paper at <https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.

Thiyanga Talagala

seer

Feature-Based Forecast Model Selection

Rob J Hyndman

George Athanasopoulos

accuracy_snaive function

<dl><dt>ts_info</dt>
<dd>list containing training and test part of a time series</dd>
<dt>function_name</dt>
<dd>function to calculate the accuracy function, the arguments of this function
should be forecast, training and test set of the time series</dd>
<dt>length_out</dt>
<dd>number of measures calculated by the function</dd></dl>

Arguments

Calculate accuracy measure based on snaive method — accuracy_snaive

<dl>

<dt>ts_info</dt>
<dd>list containing training and test part of a time series</dd>


<dt>function_name</dt>
<dd>function to calculate the accuracy function, the arguments of this function
should be forecast, training and test set of the time series</dd>


<dt>length_out</dt>
<dd>number of measures calculated by the function</dd>

</dl>

accuracy_snaive: Calculate accuracy measure based on snaive method

Description

Usage

Value

Arguments