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rebeccasalles
TSPred:Functions for Benchmarking Time Series Prediction
Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.
Maintained by Rebecca Pontes Salles. Last updated 4 years ago.
benchmarkinglinear-modelsmachine-learningnonstationaritytime-series-forecasttime-series-prediction
24 stars 5.53 score 94 scripts 1 dependents
cran
ProbSamplingI:Probabilistic Sampling Design and Strategies
It allows the user to determine sample sizes, select probabilistic samples, make estimates of different parameters for the total finite population and in studio domains, using the main design drawings.
Maintained by Jorge Barón. Last updated 1 years ago.
1.00 score