R-universe search: topic:forecasting

robjhyndman

forecast:Forecasting Functions for Time Series and Linear Models

Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

Maintained by Rob Hyndman. Last updated 7 months ago.

forecast forecasting openblas cpp

1.1k stars 17.46 score 16k scripts 240 dependents

facebook

prophet:Automatic Forecasting Procedure

Implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

Maintained by Sean Taylor. Last updated 5 months ago.

forecasting python cpp

19k stars 15.59 score 976 scripts 13 dependents

business-science

timetk:A Tool Kit for Working with Time Series

Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. Consolidates and extends time series functionality from packages including 'dplyr', 'stats', 'xts', 'forecast', 'slider', 'padr', 'recipes', and 'rsample'.

Maintained by Matt Dancho. Last updated 1 years ago.

coercion coercion-functions data-mining dplyr forecast forecasting forecasting-models machine-learning series-decomposition series-signature tibble tidy tidyquant tidyverse time time-series timeseries

626 stars 14.20 score 4.0k scripts 16 dependents

tidyverts

fable:Forecasting Models for Tidy Time Series

Provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models. These models work within the 'fable' framework provided by the 'fabletools' package, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.

Maintained by Mitchell OHara-Wild. Last updated 4 months ago.

forecasting cpp

569 stars 13.54 score 2.1k scripts 6 dependents

pecanproject

PEcAn.DB:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by David LeBauer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 11.91 score 127 scripts 27 dependents

pecanproject

PEcAn.data.atmosphere:PEcAn Functions Used for Managing Climate Driver Data

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The PECAn.data.atmosphere package converts climate driver data into a standard format for models integrated into PEcAn. As a standalone package, it provides an interface to access diverse climate data sets.

Maintained by David LeBauer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 11.63 score 64 scripts 14 dependents

epiforecasts

scoringutils:Utilities for Scoring and Assessing Predictions

Facilitate the evaluation of forecasts in a convenient framework based on data.table. It allows user to to check their forecasts and diagnose issues, to visualise forecasts and missing data, to transform data before scoring, to handle missing forecasts, to aggregate scores, and to visualise the results of the evaluation. The package mostly focuses on the evaluation of probabilistic forecasts and allows evaluating several different forecast types and input formats. Find more information about the package in the Vignettes as well as in the accompanying paper, <doi:10.48550/arXiv.2205.07090>.

Maintained by Nikos Bosse. Last updated 28 days ago.

forecast-evaluation forecasting

52 stars 11.37 score 326 scripts 7 dependents

config-i1

greybox:Toolbox for Model Building and Forecasting

Implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.

Maintained by Ivan Svetunkov. Last updated 17 days ago.

forecasting model-selection model-selection-and-evaluation regression regression-models statistics cpp

30 stars 11.03 score 97 scripts 34 dependents

pecanproject

PEcAn.visualization:PEcAn visualization functions

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. This module is used to create more complex visualizations from the data generated by PEcAn code, specifically the models.

Maintained by David LeBauer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 10.98 score 74 scripts 11 dependents

pecanproject

PEcAn.utils:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PEcAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by Rob Kooper. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 10.95 score 218 scripts 35 dependents

pecanproject

PEcAn.benchmark:PEcAn Functions Used for Benchmarking

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PEcAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. The PEcAn.benchmark package provides utilities for comparing models and data, including a suite of statistical metrics and plots.

Maintained by Mike Dietze. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 10.73 score 416 scripts 11 dependents

business-science

modeltime:The Tidymodels Extension for Time Series Modeling

The time series forecasting framework for use with the 'tidymodels' ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" (<https://otexts.com/fpp2/>). Refer to "Prophet: forecasting at scale" (<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).

Maintained by Matt Dancho. Last updated 5 months ago.

arima data-science deep-learning ets forecasting machine-learning machine-learning-algorithms modeltime prophet tbats tidymodeling tidymodels time time-series time-series-analysis timeseries timeseries-forecasting

551 stars 10.61 score 1.1k scripts 7 dependents

pecanproject

PEcAn.settings:PEcAn Settings package

Contains functions to read PEcAn settings files.

Maintained by David LeBauer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 10.03 score 54 scripts 17 dependents

pecanproject

PEcAn.assim.batch:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by Istem Fer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 9.97 score 20 scripts 2 dependents

pecanproject

PEcAn.priors:PEcAn Functions Used to Estimate Priors from Data

Functions to estimate priors from data.

Maintained by David LeBauer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 9.96 score 13 scripts 6 dependents

nicholasjclark

mvgam:Multivariate (Dynamic) Generalized Additive Models

Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.

Maintained by Nicholas J Clark. Last updated 1 days ago.

bayesian-statistics dynamic-factor-models ecological-modelling forecasting gaussian-process generalised-additive-models generalized-additive-models joint-species-distribution-modelling multilevel-models multivariate-timeseries stan time-series-analysis timeseries vector-autoregression vectorautoregression cpp

148 stars 9.92 score 117 scripts

pecanproject

PEcAn.MA:PEcAn Functions Used for Meta-Analysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. The PEcAn.MA package contains the functions used in the Bayesian meta-analysis of trait data.

Maintained by David LeBauer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 9.92 score 7 scripts 7 dependents

pecanproject

PEcAnRTM:PEcAn Functions Used for Radiative Transfer Modeling

Functions for performing forward runs and inversions of radiative transfer models (RTMs). Inversions can be performed using maximum likelihood, or more complex hierarchical Bayesian methods. Underlying numerical analyses are optimized for speed using Fortran code.

Maintained by Alexey Shiklomanov. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants fortran jags cpp

216 stars 9.72 score 132 scripts

pecanproject

PEcAn.remote:PEcAn Model Execution Utilities

This package contains utilities for communicating with and executing code on local and remote hosts. In particular, it has PEcAn-specific utilities for starting ecosystem model runs.

Maintained by Rob Kooper. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 9.69 score 13 scripts 32 dependents

pecanproject

PEcAn.logger:Logger Functions for 'PEcAn'

Convenience functions for logging outputs from 'PEcAn', the Predictive Ecosystem Analyzer (LeBauer et al. 2017) <doi:10.1890/12-0137.1>. Enables the user to set what level of messages are printed, as well as whether these messages are written to the console, a file, or both. It also allows control over whether severe errors should stop execution of the 'PEcAn' workflow; this allows strictness when debugging and lenience when running large batches of simulations that should not be terminated by errors in individual models. It is loosely based on the 'log4j' package.

Maintained by Rob Kooper. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 9.67 score 7 scripts 40 dependents

pecanproject

PEcAn.data.land:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by Mike Dietze. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 9.35 score 19 scripts 10 dependents

microsoft

finnts:Microsoft Finance Time Series Forecasting Framework

Automated time series forecasting developed by Microsoft Finance. The Microsoft Finance Time Series Forecasting Framework, aka Finn, can be used to forecast any component of the income statement, balance sheet, or any other area of interest by finance. Any numerical quantity over time, Finn can be used to forecast it. While it can be applied outside of the finance domain, Finn was built to meet the needs of financial analysts to better forecast their businesses within a company, and has a lot of built in features that are specific to the needs of financial forecasters. Happy forecasting!

Maintained by Mike Tokic. Last updated 1 months ago.

business data-science feature-selection finance finnts forecasting machine-learning microsoft time-series

194 stars 9.30 score 39 scripts

pecanproject

PEcAn.allometry:PEcAn Allometry Functions

Synthesize allometric equations or fit allometries to data.

Maintained by Mike Dietze. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 9.13 score 34 scripts

pecanproject

PEcAn.qaqc:QAQC

PEcAn integration and model skill testing

Maintained by David LeBauer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 9.07 score 5 scripts

pecanproject

PEcAn.all:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PEcAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by David LeBauer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 9.02 score 266 scripts

ramikrispin

TSstudio:Functions for Time Series Analysis and Forecasting

Provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.

Maintained by Rami Krispin. Last updated 2 years ago.

forecasting time-series timeseries tsstudio visualization

424 stars 9.00 score 656 scripts

pecanproject

PEcAn.MAAT:PEcAn Package for Integration of the MAAT Model

This module provides functions to wrap the MAAT model into the PEcAn workflows.

Maintained by Shawn Serbin. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 8.97 score 12 scripts

pecanproject

PEcAn.BIOCRO:PEcAn Package for Integration of the BioCro Model

This module provides functions to link BioCro to PEcAn.

Maintained by David LeBauer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 8.96 score 23 scripts

pecanproject

PEcAn.uncertainty:PEcAn Functions Used for Propagating and Partitioning Uncertainties in Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by David LeBauer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 8.95 score 15 scripts 5 dependents

canmod

macpan2:Fast and Flexible Compartmental Modelling

Fast and flexible compartmental modelling with Template Model Builder.

Maintained by Steve Walker. Last updated 7 days ago.

compartmental-models epidemiology forecasting mixed-effects model-fitting optimization simulation simulation-modeling cpp

4 stars 8.90 score 246 scripts 1 dependents

pecanproject

PEcAn.photosynthesis:PEcAn functions used for leaf-level photosynthesis calculations

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. The PEcAn.photosynthesis package contains functions used in the Hierarchical Bayesian calibration of the Farquhar et al 1980 model.

Maintained by Mike Dietze. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 8.87 score 19 scripts

pecanproject

PEcAn.emulator:Gausian Process Emulator

Implementation of a Gaussian Process model (both likelihood and bayesian approaches) for kriging and model emulation. Includes functions for sampling design and prediction.

Maintained by Mike Dietze. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 8.85 score 1 scripts 6 dependents

pecanproject

PEcAn.workflow:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PEcAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. This package provides workhorse functions that can be used to run the major steps of a PEcAn analysis.

Maintained by David LeBauer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 8.85 score 15 scripts 4 dependents

pecanproject

PEcAn.data.remote:PEcAn Functions Used for Extracting Remote Sensing Data

PEcAn module for processing remote data. Python module requirements: requests, json, re, ast, panads, sys. If any of these modules are missing, install using pip install <module name>.

Maintained by Bailey Morrison. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 8.77 score 6 scripts 5 dependents

pecanproject

PEcAn.ED2:PEcAn Package for Integration of ED2 Model

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. This package provides functions to link the Ecosystem Demography Model, version 2, to PEcAn.

Maintained by Mike Dietze. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 8.76 score 145 scripts

robjhyndman

fpp2:Data for "Forecasting: Principles and Practice" (2nd Edition)

All data sets required for the examples and exercises in the book "Forecasting: principles and practice" (2nd ed, 2018) by Rob J Hyndman and George Athanasopoulos <https://otexts.com/fpp2/>. All packages required to run the examples are also loaded.

Maintained by Rob Hyndman. Last updated 2 years ago.

data forecasting

106 stars 8.57 score 1.8k scripts 1 dependents

robjhyndman

fpp3:Data for "Forecasting: Principles and Practice" (3rd Edition)

All data sets required for the examples and exercises in the book "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos <https://OTexts.com/fpp3/>. All packages required to run the examples are also loaded. Additional data sets not used in the book are also included.

Maintained by Rob Hyndman. Last updated 6 months ago.

data forecasting

142 stars 8.54 score 2.5k scripts

pecanproject

PEcAn.SIPNET:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by Mike Dietze. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 8.38 score 61 scripts

pecanproject

PEcAn.LINKAGES:PEcAn Package for Integration of the LINKAGES Model

This module provides functions to link the (LINKAGES) to PEcAn.

Maintained by Ann Raiho. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 8.37 score 59 scripts

business-science

modeltime.ensemble:Ensemble Algorithms for Time Series Forecasting with Modeltime

A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability.

Maintained by Matt Dancho. Last updated 9 months ago.

ensemble ensemble-learning forecast forecasting modeltime stacking stacking-ensemble tidymodels time time-series timeseries

77 stars 8.30 score 143 scripts

robjhyndman

demography:Forecasting Mortality, Fertility, Migration and Population Data

Functions for demographic analysis including lifetable calculations; Lee-Carter modelling; functional data analysis of mortality rates, fertility rates, net migration numbers; and stochastic population forecasting.

Maintained by Rob Hyndman. Last updated 4 months ago.

actuarial demography forecasting

74 stars 8.21 score 241 scripts 6 dependents

pecanproject

PEcAnAssimSequential:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by Mike Dietze. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 8.14 score 35 scripts

nredell

forecastML:Time Series Forecasting with Machine Learning Methods

The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence 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>.

Maintained by Nickalus Redell. Last updated 5 years ago.

deep-learning direct-forecasting forecast forecasting machine-learning multi-step-ahead-forecasting neural-network python time-series

130 stars 7.64 score 134 scripts

pecanproject

PEcAn.MAESPA:PEcAn Functions Used for Ecological Forecasts and Reanalysis using MAESPA

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.This package allows for MAESPA to be run through the PEcAN workflow.

Maintained by Tony Gardella. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.59 score 2 scripts

pecanproject

PEcAn.LPJGUESS:PEcAn Package for Integration of the LPJ-GUESS Model

This module provides functions to link LPJ-GUESS to PEcAn.

Maintained by Istem Fer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants cpp

216 stars 7.59 score 1 scripts

pecanproject

PEcAn.STICS:PEcAn Package for Integration of the STICS Model

This module provides functions to link the STICS to PEcAn.

Maintained by Istem Fer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.59 score

pecanproject

PEcAn.LDNDC:PEcAn package for integration of the LDNDC model

This module provides functions to link the (LDNDC) to PEcAn.

Maintained by Henri Kajasilta. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

216 stars 7.59 score

pecanproject

PEcAn.PRELES:PEcAn Package for Integration of the PRELES Model

This module provides functions to run the PREdict Light use efficiency Evapotranspiration and Soil moisture (PRELES) model on the PEcAn project. The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool designed to simplify the management of model parameterization,execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by Tony Gardella. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.59 score 4 scripts

pecanproject

PEcAn.SIBCASA:PEcAn Package for Integration of the SiBCASA Model

This module provides functions to link (SiBCASA) to PEcAn. It is a work in progress and is not yet fully functional.

Maintained by Rob Kooper. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.59 score

pecanproject

PEcAn.ModelName:PEcAn Package for Integration of the ModelName Model

This module provides functions to link the (ModelName) to PEcAn.

Maintained by Jane Doe. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.59 score

pecanproject

PEcAn.data.mining:PEcAn Functions Used for Exploring Model Residuals and Structures

(Temporary description) PEcAn functions used for exploring model residuals and structures.

Maintained by Mike Dietze. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.59 score 1 scripts

pecanproject

PEcAn.JULES:PEcAn Package for Integration of the JULES Model

This module provides functions to link the (JULES) to PEcAn.

Maintained by Mike Dietze. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.59 score

pecanproject

PEcAn.GDAY:PEcAn Package for Integration of the GDAY Model

This module provides functions to link the GDAY model to PEcAn.

Maintained by Martin De Kauwe. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.59 score 1 scripts

pecanproject

PEcAn.dvmdostem:PEcAn Package for Integration of the Dvmdostem Model

This module provides functions to link the dvmdostem model to PEcAn.

Maintained by Tobey Carman. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.59 score 3 scripts

pecanproject

PEcAn.FATES:PEcAn Package for Integration of FATES Model

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. This package provides functions to link the FATES model to PEcAn.

Maintained by Mike Dietze. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.59 score 6 scripts

pecanproject

PEcAn.DALEC:PEcAn Package for Integration of the DALEC Model

This module provides functions to link DALEC to PEcAn.

Maintained by Mike Dietze. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.59 score 8 scripts

pecanproject

PEcAn.CLM45:PEcAn Package for Integration of CLM4.5 Model

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. This package provides functions to link the Community Land Model, version 4.5, to PEcAn.

Maintained by Mike Dietze. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.59 score 1 scripts

pecanproject

PEcAn.CABLE:PEcAn package for integration of the CABLE model

This module provides functions to link the (CABLE) to PEcAn.

Maintained by Tony Gardella. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

216 stars 7.59 score

pecanproject

PEcAn.BASGRA:PEcAn Package for Integration of the BASGRA Model

This module provides functions to link the BASGRA model to PEcAn.

Maintained by Istem Fer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants fortran glibc

216 stars 7.59 score 1 scripts

spsanderson

healthyR.ts:The Time Series Modeling Companion to 'healthyR'

Hospital time series data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative time series hospital data. Some of these include average length of stay, and readmission rates. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.

Maintained by Steven Sanderson. Last updated 6 months ago.

ai arima-forecasting arima-model ets forecasting ggplot2 machine-learning modeling prophet time-series time-series-analysis workflows

19 stars 7.58 score 56 scripts 1 dependents

robjhyndman

Mcomp:Data from the M-Competitions

The 1001 time series from the M-competition (Makridakis et al. 1982) <DOI:10.1002/for.3980010202> and the 3003 time series from the IJF-M3 competition (Makridakis and Hibon, 2000) <DOI:10.1016/S0169-2070(00)00057-1>.

Maintained by Rob Hyndman. Last updated 9 months ago.

forecasting time-series

11 stars 7.00 score 288 scripts 2 dependents

business-science

modeltime.resample:Resampling Tools for Time Series Forecasting

A 'modeltime' extension that implements forecast resampling tools that assess time-based model performance and stability for a single time series, panel data, and cross-sectional time series analysis.

Maintained by Matt Dancho. Last updated 1 years ago.

accuracy-metrics backtesting bootstrap bootstrapping cross-validation forecasting modeltime modeltime-resample resampling statistics tidymodels time-series

19 stars 6.64 score 38 scripts 1 dependents

ygeunkim

bvhar:Bayesian Vector Heterogeneous Autoregressive Modeling

Tools to model and forecast multivariate time series including Bayesian Vector heterogeneous autoregressive (VHAR) model by Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>). 'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.

Maintained by Young Geun Kim. Last updated 1 months ago.

bayesian bayesian-econometrics bvar eigen forecasting har pybind11 python rcppeigen time-series vector-autoregression cpp openmp

6 stars 6.35 score 25 scripts

danigiro

FoReco:Forecast Reconciliation

Classical (bottom-up and top-down), optimal combination and heuristic point (Di Fonzo and Girolimetto, 2023 <doi:10.1016/j.ijforecast.2021.08.004>) and probabilistic (Girolimetto et al. 2023 <doi:10.1016/j.ijforecast.2023.10.003>) forecast reconciliation procedures for linearly constrained time series (e.g., hierarchical or grouped time series) in cross-sectional, temporal, or cross-temporal frameworks.

Maintained by Daniele Girolimetto. Last updated 3 months ago.

forecasting reconciliation time-series

33 stars 6.19 score 104 scripts

dylanb95

statespacer:State Space Modelling in 'R'

A tool that makes estimating models in state space form a breeze. See "Time Series Analysis by State Space Methods" by Durbin and Koopman (2012, ISBN: 978-0-19-964117-8) for details about the algorithms implemented.

Maintained by Dylan Beijers. Last updated 2 years ago.

cpp dynamic-linear-model forecasting gaussian-models kalman-filter mathematical-modelling state-space statistical-inference statistical-models structural-analysis time-series openblas cpp openmp

15 stars 6.14 score 37 scripts

ahaeusser

echos:Echo State Networks for Time Series Modeling and Forecasting

Provides a lightweight implementation of functions and methods for fast and fully automatic time series modeling and forecasting using Echo State Networks (ESNs).

Maintained by Alexander Häußer. Last updated 27 days ago.

echo-state-networks fable fabletools forecast forecasting recurrent-neural-networks reservoir-computing ridge-regression time-series openblas cpp openmp

12 stars 6.03 score 8 scripts

akai01

caretForecast:Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms

Conformal time series forecasting using the caret infrastructure. It provides access to state-of-the-art machine learning models for forecasting applications. The hyperparameter of each model is selected based on time series cross-validation, and forecasting is done recursively.

Maintained by Resul Akay. Last updated 2 years ago.

caret conformal-prediction data-science econometrics forecast forecasting forecasting-models machine-learning macroeconometrics microeconometrics time-series time-series-forcasting time-series-prediction

25 stars 5.62 score 28 scripts 4 dependents

mamut86

diffusion:Forecast the Diffusion of New Products

Various diffusion models to forecast new product growth. Currently the package contains Bass, Gompertz, Gamma/Shifted Gompertz and Weibull curves. See Meade and Islam (2006) <doi:10.1016/j.ijforecast.2006.01.005>.

Maintained by Oliver Schaer. Last updated 12 months ago.

diffusion diffusion-models forecasting growth-curve-models lifecycle new-product

8 stars 5.54 score 29 scripts

gmgeorg

ForeCA:Forecastable Component Analysis

Implementation of Forecastable Component Analysis ('ForeCA'), including main algorithms and auxiliary function (summary, plotting, etc.) to apply 'ForeCA' to multivariate time series data. 'ForeCA' is a novel dimension reduction (DR) technique for temporally dependent signals. Contrary to other popular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time dependency explicitly into account and searches for the most ''forecastable'' signal. The measure of forecastability is based on the Shannon entropy of the spectral density of the transformed signal.

Maintained by Georg M. Goerg. Last updated 5 years ago.

blind-source-separation dimensionality-reduction forecasting multivariate-timeseries signal-processing spectrum time-series time-series-analysis

15 stars 5.47 score 39 scripts

haeran-cho

fnets:Factor-Adjusted Network Estimation and Forecasting for High-Dimensional Time Series

Implements methods for network estimation and forecasting of high-dimensional time series exhibiting strong serial and cross-sectional correlations under a factor-adjusted vector autoregressive model. See Barigozzi, Cho and Owens (2024) <doi:10.1080/07350015.2023.2257270> for further descriptions of FNETS methodology and Owens, Cho and Barigozzi (2024) <arXiv:2301.11675> accompanying the R package.

Maintained by Haeran Cho. Last updated 4 months ago.

factor-models forecasting high-dimensional network-estimation time-series vector-autoregression cpp

7 stars 5.20 score 28 scripts

tsmodels

tstests:Time Series Goodness of Fit and Forecast Evaluation Tests

Goodness of Fit and Forecast Evaluation Tests for timeseries models. Includes, among others, the Generalized Method of Moments (GMM) Orthogonality Test of Hansen (1982), the Nyblom (1989) parameter constancy test, the sign-bias test of Engle and Ng (1993), and a range of tests for value at risk and expected shortfall evaluation.

Maintained by Alexios Galanos. Last updated 5 months ago.

forecasting statistical-tests

5 stars 5.10 score 3 scripts

nelson-n

lmForc:Linear Model Forecasting

Introduces in-sample, out-of-sample, pseudo out-of-sample, and benchmark model forecast tests and a new class for working with forecast data, Forecast.

Maintained by Nelson Rayl. Last updated 7 months ago.

forecasting linear-models

6 stars 5.08 score 20 scripts

jobnmadu

Dyn4cast:Dynamic Modeling and Machine Learning Environment

Estimates, predict and forecast dynamic models as well as Machine Learning metrics which assists in model selection for further analysis. The package also have capabilities to provide tools and metrics that are useful in machine learning and modeling. For example, there is quick summary, percent sign, Mallow's Cp tools and others. The ecosystem of this package is analysis of economic data for national development. The package is so far stable and has high reliability and efficiency as well as time-saving.

Maintained by Job Nmadu. Last updated 15 days ago.

data-science equal-lenght-forecast forecasting knots machine-learning nigeria prediction regression-models spline-models statistics time-series

4 stars 5.03 score 38 scripts

dkesada

dbnR:Dynamic Bayesian Network Learning and Inference

Learning and inference over dynamic Bayesian networks of arbitrary Markovian order. Extends some of the functionality offered by the 'bnlearn' package to learn the networks from data and perform exact inference. It offers three structure learning algorithms for dynamic Bayesian networks: Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014) <doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021) <doi:10.1007/978-3-030-86271-8_14>. It also offers the possibility to perform forecasts of arbitrary length. A tool for visualizing the structure of the net is also provided via the 'visNetwork' package.

Maintained by David Quesada. Last updated 10 months ago.

bayesian-networks dynamic-bayesian-networks forecasting inference time-series cpp

55 stars 5.01 score 37 scripts

tylerjpike

OOS:Out-of-Sample Time Series Forecasting

A comprehensive and cohesive API for the out-of-sample forecasting workflow: data preparation, forecasting - including both traditional econometric time series models and modern machine learning techniques - forecast combination, model and error analysis, and forecast visualization.

Maintained by Tyler J. Pike. Last updated 4 years ago.

econometrics forecast-combination forecasting machine-learning

9 stars 4.95 score 5 scripts

tylerjpike

sovereign:State-Dependent Empirical Analysis

A set of tools for state-dependent empirical analysis through both VAR- and local projection-based state-dependent forecasts, impulse response functions, historical decompositions, and forecast error variance decompositions.

Maintained by Tyler J. Pike. Last updated 2 years ago.

econometrics forecasting impulse-response local-projection macroeconomics state-dependent time-series vector-autoregression

12 stars 4.78 score 8 scripts

techtonique

ahead:Time Series Forecasting with uncertainty quantification

Univariate and multivariate time series forecasting with uncertainty quantification.

Maintained by T. Moudiki. Last updated 1 months ago.

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

21 stars 4.63 score 51 scripts

epiforecasts

stackr:Create Mixture Models From Predictive Samples

The `stackr` package provides an easy way to combine predictions from individual time series or panel data models to an ensemble. `stackr` stacks (Yuling Yao, Aki Vehtari, Daniel Simpson, and Andrew Gelman (2018) <doi:10.1214/17-BA1091>) Models according to the Continuous Ranked Probability Score (CRPS) (Tilmann Gneiting & Adrian E Raftery (2007) <doi:10.1198/016214506000001437>) over k-step ahead predictions. It is therefore especially suited for timeseries and panel data. A function for leave-one-out CRPS may be added in the future. Predictions need to be predictive distributions represented by predictive samples. Usually, these will be sets of posterior predictive simulation draws generated by an MCMC algorithm. Given some training data with true observed values as well as predictive samples generated from different models, `crps_weights` finds the optimal (in the sense of minimizing expected cross-validation predictive error) weights to form an ensemble from these models. Using these weights, `mixture_from_samples` can then provide samples from the optimal model mixture by drawing from the predictice samples of the individual models in the correct proportion. This gives a mixture model solely based on predictive samples and is in this regard superior to other ensembling techniques like Bayesian Model Averaging.

Maintained by Nikos Bosse. Last updated 5 months ago.

crps ensembles forecasting stacking cpp

5 stars 4.34 score 44 scripts

alsabtay

ATAforecasting:Automatic Time Series Analysis and Forecasting using the Ata Method

The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>, Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation.

Maintained by Ali Sabri Taylan. Last updated 2 years ago.

ata ataforecasting fable forecast forecasting time-series cpp

5 stars 3.88 score 4 scripts 1 dependents

lehmasve

hdflex:High-Dimensional Aggregate Density Forecasts

Provides a forecasting method that efficiently maps vast numbers of (scalar-valued) signals into an aggregate density forecast in a time-varying and computationally fast manner. The method proceeds in two steps: First, it transforms a predictive signal into a density forecast and, second, it combines the resulting candidate density forecasts into an ultimate aggregate density forecast. For a detailed explanation of the method, please refer to Adaemmer et al. (2023) <doi:10.2139/ssrn.4342487>.

Maintained by Sven Lehmann. Last updated 5 months ago.

ensemble-learning forecast-combination forecasting high-dimensionality time-series openblas cpp openmp

3 stars 3.78 score 1 scripts

akai01

ngboostForecast:Probabilistic Time Series Forecasting

Probabilistic time series forecasting via Natural Gradient Boosting for Probabilistic Prediction.

Maintained by Resul Akay. Last updated 3 years ago.

forecasting machine-learning ngboost ngboost-forecast probabilistic-forecasts python sklearn time-series

7 stars 3.69 score 14 scripts

alsabtay

fable.ata:'ATAforecasting' Modelling Interface for 'fable' Framework

Allows ATA (Automatic Time series analysis using the Ata method) models from the 'ATAforecasting' package to be used in a tidy workflow with the modeling interface of 'fabletools'. This extends 'ATAforecasting' to provide enhanced model specification and management, performance evaluation methods, and model combination tools. The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>, Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal).

Maintained by Ali Sabri Taylan. Last updated 2 years ago.

ataforecasting fable fabletools forecast forecasting

4 stars 3.30 score 9 scripts

mtrupiano1

knnwtsim:K Nearest Neighbor Forecasting with a Tailored Similarity Metric

Functions to implement K Nearest Neighbor forecasting using a weighted similarity metric tailored to the problem of forecasting univariate time series where recent observations, seasonal patterns, and exogenous predictors are all relevant in predicting future observations of the series in question. For more information on the formulation of this similarity metric please see Trupiano (2021) <arXiv:2112.06266>.

Maintained by Matthew Trupiano. Last updated 3 years ago.

forecasting knn-regression machine-learning time-series

1 stars 2.70 score 2 scripts