Man pages for weecology/MATSS-forecasting
Analysis and Synthesis of Forecasting Ecological Time Series

arima_tsMake forecasts using AR / ARIMA
build_methods_planMake a drake plan with forecasting methods to be compared
build_ward_data_planMake a drake plan with all the datasets in Ward et al. 2014
build_ward_methods_planMake a drake plan with all the forecasting methods in Ward et...
check_time_seriesCheck time series for potentially problematic features
compute_subset_rangeCompute the indices corresponding to a defined subset
embeddEmbed a time series
ets_tsExponentially smoothed time series model
expect_forecastsCheck if object is in valid forecasts format
expect_NA_warningsCheck if warnings are expected for a too-short time series
forecast_iteratedIterated one-step forecasting (no refitting)
gam_tsMake forecasts using a Generalized Additive Model
gausspr_tsMake forecasts using gaussian process regression
get_LPI_dataRead in the LPI time series
get_ward_dataRead in a specific database from Ward et al. 2014
hindcastHindcasting with one-step forecasts
lm_tsMake forecasts using a Linear Model
locreg_tsMake forecasts using locally weighted regression
marss_tsMake forecasts using a state space model
naive_one_stepNaive one-step ahead forecast
nnet_tsMake forecasts using neural network time series model
npreg_tsMake forecasts using nonparametric kernel regression
PECalculate the permuation entropy of a times series
pipePipe operator
randomwalk_tsForecast using a random walk model
ranfor_tsMake forecasts using a random forest model
reshape_ward_dataReshape the processed data file from Ward et al. 2014
simplex_tsMake forecasts using simplex projection or S-maps
sts_tsStructural time series model
word_distributionCompute the distribution of "word"s in a time series.
weecology/MATSS-forecasting documentation built on Nov. 7, 2019, 4:41 a.m.