R-universe search: "time-series-forecast"

Showing 4 of total 4 results (show query)

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.

benchmarking linear-models machine-learning nonstationarity time-series-forecast time-series-prediction

140.4 match 24 stars 5.53 score 94 scripts 1 dependents

techtonique

ahead:Time Series Forecasting with uncertainty quantification

Univariate and multivariate time series forecasting with uncertainty quantification.

Maintained by T. Moudiki. Last updated 27 days ago.

forecasting machine-learning predictive-modeling statistical-learning time-series time-series-forecasting uncertainty-quantification cpp

104.1 match 21 stars 4.77 score 51 scripts

krzjoa

m5:'M5 Forecasting' Challenges Data

Contains functions, which facilitate downloading, loading and preparing data from 'M5 Forecasting' challenges (by 'University of Nicosia', hosted on 'Kaggle'). The data itself is set of time series of different product sales in 'Walmart'. The package also includes a ready-to-use built-in M5 subset named 'tiny_m5'. For detailed information about the challenges, see: Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilis. (2020). The M5 Accuracy competition: Results, findings and conclusions. <doi:10.1016/j.ijforecast.2021.10.009>

Maintained by Krzysztof Joachimiak. Last updated 3 years ago.

data-science kaggle-competition kaggle-dataset m5-competition m5-forecasting time-series-forecasting walmart walmart-sales-forecasting

42.4 match 2 stars 4.45 score 28 scripts

grasia

knnp:Time Series Prediction using K-Nearest Neighbors Algorithm (Parallel)

Two main functionalities are provided. One of them is predicting values with k-nearest neighbors algorithm and the other is optimizing the parameters k and d of the algorithm. These are carried out in parallel using multiple threads.

Maintained by Daniel Bastarrica Lacalle. Last updated 5 years ago.

knearest-neighbor-algorithm parallel time-series-forecasting

41.6 match 1 stars 2.70 score 8 scripts