data_buoy

A dataset containing daily average sensor measurements of several environmental
conditions collected by 14 buoys in Lake Michigan from 2012 through 2018.

datasets

The purpose of 'forecastML' is to simplify the process of multi-step-ahead direct forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.

Nickalus Redell

forecastML

Time Series Forecasting with Machine Learning Methods

data_buoy function

A data.frame with 30,821 rows and 9 columns:<dl class="dl-horizontal">
 <dt>date</dt><dd>date</dd>
 <dt>wind_spd</dt><dd>average daily wind speed in kts</dd>
 <dt>buoy_id</dt><dd>the station ID for each buoy</dd>
 <dt>lat</dt><dd>latitude</dd>
 <dt>lon</dt><dd>longitude</dd>
 <dt>day</dt><dd>day of year</dd>
 <dt>year</dt><dd>calendar year</dd>
 <dt>air_temperature</dt><dd>air temperature in degrees Fahrenheit</dd>
 <dt>sea_surface_temperature</dt><dd>water temperature in degrees Fahrenheit</dd>
</dl>

Format

NOAA buoy weather data — data_buoy

A data.frame with 30,821 rows and 9 columns:<dl class='dl-horizontal'>
 <dt>date</dt><dd>date</dd>
 <dt>wind_spd</dt><dd>average daily wind speed in kts</dd>
 <dt>buoy_id</dt><dd>the station ID for each buoy</dd>
 <dt>lat</dt><dd>latitude</dd>
 <dt>lon</dt><dd>longitude</dd>
 <dt>day</dt><dd>day of year</dd>
 <dt>year</dt><dd>calendar year</dd>
 <dt>air_temperature</dt><dd>air temperature in degrees Fahrenheit</dd>
 <dt>sea_surface_temperature</dt><dd>water temperature in degrees Fahrenheit</dd>
</dl>

data_buoy: NOAA buoy weather data

Description

Usage

Arguments

Format