Appendix B. Software
Table A.1: A list of indicative free or open-source packages, libraries, and toolboxes linking to the theory sections of this article. The authors assume no liability for the software listed below; interested users are strongly advised to read the respective documentations and licences terms.
| Related section | Software | Package/Library/Toolbox | Function(s) | Comments |
|---|---|---|---|---|
| §2.2.1. Box-Cox transformations | R | forecast | BoxCox; InvBoxCox; BoxCox.lambda; | Functions to transform the input variable using a Box-Cox transformation, reverse the transformation and find optimal parameters. https://cran.r-project.org/package=forecast |
| §2.2.2. Box-Cox transformations Time series decomposition | R | stats | decompose; stl | Classical decomposition method (additive and multiplicative), and STL decomposition method. https://stat.ethz.ch/R-manual/R-devel/library/stats/html/00Index.html |
| R | forecast | seasadj; seasonal; mstl; msts; tbats.components | Tools for extracting components, and multiple seasonal decomposition methods. https://cran.r-project.org/package=forecast | |
| R | tsutils | decomp; seasplot | Classical decomposition method, and functions for seasonal plots. https://cran.r-project.org/package=tsutils | |
| R | stR | AutoSTR; STR; heuristicSTR; plot.STR; seasadj.STR | Seasonal-Trend decomposition based on Regression. https://cran.r-project.org/package=stR | |
| R | seasonal | seas | Functions for X-11, SEATS, and X-13-ARIMA-SEATS decomposition methods. https://cran.r-project.org/package=seasonal | |
| Gretl | buys_ballot | Plot seasonal time series components. http://gretl.sourceforge.net/ | ||
| Gretl | season_plot | set_season_plot | Plot seasonal time-series components. http://gretl.sourceforge.net/ | |
| Gretl | tsfcst | decompcl | Classical time series decomposition. http://gretl.sourceforge.net/ | |
| Gretl | StrucTiSM | STSM_components | Decomposition using structural timeseries model. http://gretl.sourceforge.net/ | |
| §2.2.3. Anomaly detection and time series forecasting | R | anomalize | time_decompose; anomalize; time_recompose | A “tidy” workflow for detecting anomalies in data. https://cran.r-project.org/package=anomalize |
| R | oddstream | find_odd_streams; extract_tsfeatures; set_outlier_threshold | Early detection of anomalous series within a large collection of streaming time series data. https://cran.r-project.org/package=oddstream | |
| R | tsoutliers | tso; locate.outliers.oloop; remove.outliers | Detection of outliers in time series such as Innovational outliers, additive outliers, level shifts, temporary changes and seasonal level shifts. https://cran.r-project.org/package=tsoutliers | |
| R | stray | find_HDoutliers; find_threshold; display_HDoutliers | Anomaly detection in high dimensional and temporal data. https://cran.r-project.org/package=stray | |
| R | forecast | tsoutliers; tsclean | Provides some simple heuristic methods for identifying and correcting outliers. https://cran.r-project.org/package=seasonal | |
| R | OutliersO3 | OutliersO3; O3plotM; O3plotT; O3prep | Draws overview of outliers (O3) Plots. https://cran.r-project.org/package=OutliersO3 | |
| R | CRAN Task View | Anomaly Detection with R | Contains a list of R packages that can be used for anomaly detection. https://github.com/pridiltal/ctv-AnomalyDetection | |
| Gretl | tramolin | Outlier detection/correction and missing data interpolation. http://gretl.sourceforge.net/ | ||
| §2.2.4. Robust handling of outliers in time series forecasting | R | gets | isat | Function for running impulse and step indicator saturation. https://cran.r-project.org/package=gets |
| §2.3.1. Exponential smoothing models | R | forecast | ets; forecast.ets; ses; | Functions for simple exponential smoothing and automatic exponential smoothing modelling. https://cran.r-project.org/package=forecast |
| R | smooth | es | Function for automatic exponential smoothing modelling. https://cran.r-project.org/package=smooth | |
| Gretl | tsfcst | expsmpars | Simple exponential smoothing minimising the sum of squared errors. http://gretl.sourceforge.net/ | |
| §2.3.2. Time-series regression models | R | stats | lm | Fitting linear regression models. https://stat.ethz.ch/R-manual/R-devel/library/stats/html/00Index.html |
| R | leaps | regsubsets | Functions for selecting linear regression models. https://cran.r-project.org/package=leaps | |
| R | relaimpo | Relative importance of regressors in linear models. https://cran.r-project.org/package=relaimpo | ||
| R | MASS | stepAIC | Choose a model by AIC in a stepwise algorithm. https://cran.r-project.org/package=MASS | |
| Gretl | ols, lad, midasreg | Regression models with OLS, LAD, and MIDAS with functionality for forecasting. http://gretl.sourceforge.net/ | ||
| §2.3.3. Theta method and models | R | forecast | thetaf | Returns forecasts and prediction intervals for a theta method forecast. https://cran.r-project.org/package=forecast |
| R | forecTheta | stheta; stm; otm; dstm; dotm | Functions for forecasting univariate time series using Theta Models. https://cran.r-project.org/package=forecTheta | |
| R | tsutils | theta | Estimate Theta method. https://cran.r-project.org/package=tsutils | |
| Gretl | tsfcst | stheta | Theta-method for univariate forecasting. http://gretl.sourceforge.net/ | |
| §2.3.4. Autoregressive integrated moving average (ARIMA) models | R | forecast | auto.arima; Arima; arfima; arima.errors; arimaorder | Functions for fitting and forecasting with ARIMA models. https://cran.r-project.org/package=forecast |
| R | smooth | auto.msarima; auto.ssarima; msarima; ssarima | State-space and multiple seasonalities implementations of ARIMA models. https://cran.r-project.org/package=smooth | |
| Gretl | arima | Functions for fitting and forecasting with SARIMAX models. http://gretl.sourceforge.net/ | ||
| Gretl | auto_arima | Find best fitting SARIMAX model with functions for forecasting. http://gretl.sourceforge.net/ | ||
| Gretl | armax | Automatically determine the best ARMAX model. http://gretl.sourceforge.net/ | ||
| R | aTSA | adf.test | Augmented Dickey-Fuller test. https://cran.r-project.org/package=aTSA | |
| R | tseries | kpss.test | Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test. https://cran.r-project.org/package=tseries | |
| R | forecast | ndiffs; nsdiffs | Estimates the number of (seasonal) differences required to make a given time series stationary. https://cran.r-project.org/package=forecast | |
| Gretl | adf; adf-gls; kpss; levinlin | Various unit-root tests for time-series and panel data. http://gretl.sourceforge.net/ | ||
| §2.3.4. Autoregressive integrated moving average (ARIMA) models (continued) | Gretl | DHF_test | Package for Dickey-Hasza-Fuller seasonal Unit Root Test. http://gretl.sourceforge.net/ | |
| Gretl | DP | Package for testing for a double unit root. http://gretl.sourceforge.net/ | ||
| Gretl | GHegy | Package Seasonal unit roots tests. http://gretl.sourceforge.net/ | ||
| Gretl | Kapetanios | Package for Kapetanios’ unit root test with possible structural breaks. http://gretl.sourceforge.net/ | ||
| Gretl | PPtest | Package for running Phillips-Perron unit root test. http://gretl.sourceforge.net/ | ||
| Gretl | VSG_test | Package for test proposed by Ventosa-Santaulària and Gómez-Zaldívar. http://gretl.sourceforge.net/ | ||
| §2.3.5. Forecasting for multiple seasonal cycles | R | smooth | msarima; ssarima | Functions for forecasting data with multiple seasonal cycles. https://cran.r-project.org/package=smooth |
| R | fable | model; forecast; fasster; ETS; ARIMA; TSLM | Forecasting models for tidy time series. https://cran.r-project.org/package=fable | |
| R, Python | prophet | Facebook’s automatic forecasting procedure. https://cran.r-project.org/package=prophet | ||
| R | tidymodels | Collection of packages for modelling and machine learning using tidyverse principles. https://cran.r-project.org/package=tidymodels | ||
| R | forecast | tbats; dshw | Functions for forecasting data with multiple seasonal cycles. https://cran.r-project.org/package=forecast | |
| R | fable.prophet | prophet; forecast | A tidy R interface to the prophet forecasting procedure using fable. https://github.com/mitchelloharawild/fable.prophet | |
| §2.3.6. State-space models | Matlab | SSpace | General modelling of linear, non-linear and non-Gaussian State Space systems. https://github.com/djpedregal/SSpace | |
| Matlab | SSM | General modelling of linear, non-linear and non-Gaussian State Space systems. https://www.mathworks.com/help/econ/ssm-class.html | ||
| Matlab | SSMMATLAB | A Set of MATLAB Programs for the Statistical Analysis of State Space Models. https://github.com/vgomezenriquez/ssmmatlab | ||
| Matlab | E4 | A MATLAB toolbox for time series analysis in State Space form. https://www.ucm.es/e-4 | ||
| R | UComp | Automatic identification of Unobserved Components models in State Space form. https://cran.r-project.org/package=UComp | ||
| R | statespacer | State Space modelling, mainly ARIMA and Basic Structural Models. https://cran.r-project.org/package=statespacer | ||
| R | smooth | Forecasting using single error State Space models. https://cran.r-project.org/package=smooth | ||
| R | bssm | Bayesian Inference of Non-Gaussian State Space Models. https://cran.r-project.org/package=bssm | ||
| R | mssm | Multivariate State Space models. https://cran.r-project.org/package=mssm | ||
| R | KFAS | Kalman Filter and Smoother for Exponential Family State Space Models. https://cran.r-project.org/package=KFAS | ||
| §2.3.6. State-space models (continued) | R | TSSS | Time Series Analysis with State Space Model, based on the methods in Kitagawa (1993). https://cran.r-project.org/package=TSSS | |
| R | dlm | Bayesian and Likelihood Analysis of Dynamic Linear Models (Gaussian State Space models). https://cran.r-project.org/package=dlm | ||
| Python | statsmodels | statespace | Time Series Analysis by State Space Methods. https://www.statsmodels.org/stable/index.html | |
| Gretl | kfilter; ksmooth; kdsmooth; ksimul | State Space Modeling functionality with function for forecasting. http://gretl.sourceforge.net/ | ||
| Gretl | StrucTiSM | STSM_fcast | Harvey-style Structural Time Series Models with function for forecasting. http://gretl.sourceforge.net/ | |
| §2.3.7. Models for population processes | R | dembase | General-purpose tools for demographic analysis. https://github.com/StatisticsNZ/dembase | |
| R | demest | Bayesian statistical methods for demography. https://github.com/StatisticsNZ/demest | ||
| R | demlife | Creating and working with life tables. https://github.com/StatisticsNZ/demlife | ||
| R | BayesPop | Generating population projections for all countries of the world using several probabilistic components, such as total fertility rate and life expectancy. https://cran.r-project.org/package=bayesPop | ||
| R | bayesTFR | Making probabilistic projections of total fertility rate for all countries of the world, using a Bayesian hierarchical model. https://cran.r-project.org/package=bayesTFR | ||
| R | bayesLife | Making probabilistic projections of life expectancy for all countries of the world, using a Bayesian hierarchical model. https://cran.r-project.org/package=bayesLife | ||
| Spreadsheet | DAPPS | Demographic Analysis and Population Projection System: Standalone spreadsheet-based software for demographic estimation and projections, prepared by the US Census Bureau. https://www.census.gov/data/software/dapps.Overview.html | ||
| §2.3.9. Forecasting with many variables | R | gets | getsm, getsv, isat, isatvar | Package that implements general to specific model selection, indicator saturation, with functionality for forecasting. https://cran.r-project.org/package=gets |
| R | vars | Functions and routines for VAR Modelling. https://cran.r-project.org/package=vars | ||
| Gretl | var; system | Fitting system-models with functionality for forecasting. http://gretl.sourceforge.net/ | ||
| §2.3.10. Functional time series models | R | ftsa | ftsm; farforecast; T_stationarity | Functional time series analysis. https://cran.r-project.org/package=ftsa |
| §2.3.11. ARCH/GARCH models | R | tseries | garch | Fit GARCH models to time series. https://cran.r-project.org/package=tseries |
| Python | PyFlux | Time series analysis and prediction tools that focus on autoregressive methods (ARIMA, ARCH, GARCH, etc). https://pyflux.readthedocs.io/en/latest/index.html | ||
| Gretl | arch, garch | Fit (G)ARCH models to time series. http://gretl.sourceforge.net/ | ||
| §2.3.11. ARCH/GARCH models (continued) | Gretl | gig | gig_estimate; gig_var_fcast | Estimate various types of GARCH models. http://gretl.sourceforge.net/ |
| §2.3.12. Markov switching models | R | MSwM | Fitting Markov switching models. https://cran.r-project.org/package=MSwM | |
| R | NHMSAR | Non-homogeneous Markov switching autoregressive models. https://cran.r-project.org/package=NHMSAR | ||
| §2.3.13. Threshold models | R | TAR | Bayesian modelling of autoregressive threshold time series models. https://cran.r-project.org/package=TAR | |
| R | TSA | tar; star | Functions for threshold models (and general time series analysis). https://cran.r-project.org/package=TSA | |
| Gretl | Threshold_Panel | THRESH_SETUP | Hansen’s panel threshold model. http://gretl.sourceforge.net/ | |
| Gretl | SETAR | Estimation of a SETAR model. http://gretl.sourceforge.net/ | ||
| §2.3.15. Forecasting with DSGE models | R | BMR | forecast | Bayesian Macroeconometrics in R (BMR) is a package for estimating and forecasting Bayesian VAR and DSGE. https://github.com/kthohr/BMR |
| Matlab/GNU Octave | Dynare | Software platform for solving, estimating, and making forecasts with DSGE. https://www.dynare.org/ | ||
| §2.3.18. Innovation diffusion models | R | DIMORA | Estimation of Bass Model, Generalised Bass Model, GGM, UCRCD. https://cran.r-project.org/package=DIMORA | |
| R | diffusion | diffusion | Various diffusion models to forecast new product growth. Currently the package contains Bass, Gompertz and Gamma/Shifted Gompertz curves. https://cran.r-project.org/package=diffusion | |
| §2.3.19. The natural law of growth in competition | R | LS2Wstat | scurve | An S curve function between two constant values. https://cran.r-project.org/package=LS2Wstat |
| §2.3.21. Estimation and representation of uncertainty | R | hdrcde | cde | Conditional kernel density estimation to produce marginal distributions (uncertainty forecasts). https://cran.r-project.org/package=hdrcde |
| R | gamlss | gamlss | Semi-parametric models for uncertainty forecasting. https://cran.r-project.org/package=gamlss | |
| R | gamboostLSS | mboostLSS | Semi-parametric component-wise gradient boosting models for uncertainty forecasting. https://cran.r-project.org/package=gamboostLSS | |
| Python | scikit-learn | GradientBoostingRegressor; RandomForestQuantileRegressor | Machine learning models (gradient boosting trees and random forests) for quantile forecasting. https://scikit-learn.org/stable/ | |
| R | quantreg | rq; lprq; nlqr | Estimation and inference methods for models of conditional quantiles. https://cran.r-project.org/package=quantreg | |
| R | EnvStats | FcnsByCatPredInts; pointwise | Functions for computing prediction intervals and simultaneous prediction intervals. https://cran.r-project.org/package=EnvStats | |
| R | rmgarch | dccfit-methods; dccforecast-methods | Multivariate GARCH Models (e.g., forecasting covariance matrix). https://cran.r-project.org/package=rmgarch | |
| §2.3.22. Forecasting under fat tails | R | FatTailsR | Functions for Kiener distributions and fat tails. https://cran.r-project.org/package=FatTailsR | |
| §2.4.3. Bayesian forecasting with copulas | R | VineCopula | Statistical analysis of vine copula models. https://cran.r-project.org/package=VineCopula | |
| R | cdcopula | Covariate-dependent copula models. https://github.com/feng-li/cdcopula | ||
| §2.4.3. Bayesian forecasting with copulas (continued) | R | FactorCopula | Factor Copula Models for Mixed Continuous and Discrete Data. https://cran.r-project.org/package=FactorCopula | |
| §2.5.1. Leading indicators and Granger causality | R | lmtest | grangertest | Test for Granger causality. https://cran.r-project.org/package=lmtest |
| Gretl | var; omit | Standard Granger-causality test. http://gretl.sourceforge.net/ | ||
| Gretl | BreitungCandelonTest | Breitung-Candelon test of frequency-wise Granger (non-)causality. http://gretl.sourceforge.net/ | ||
| §2.5.3. Variable Selection | R | glmnet | Generalised linear models with Lasso or elastic net regularisation. https://cran.r-project.org/package=glmnet | |
| Gretl | omit | Sequential removing of variables to a model. http://gretl.sourceforge.net/ | ||
| Gretl | addlist | Sequential addition of variables to a model. http://gretl.sourceforge.net/ | ||
| Gretl | regls | Add-on for regularised least squares such as Ridge, Lasso and Elastic-Net. http://gretl.sourceforge.net/ | ||
| Gretl | fsboost | fsreg | Forward-stagewise boosted regression estimates with functionality for forecasting. http://gretl.sourceforge.net/ | |
| §2.5.4. Model Selection | R | gets | Functions for automatic general to specific model selection. https://cran.r-project.org/package=gets | |
| §2.5.5. Cross-validation for time-series data | R | forecast | CVar | k-fold Cross-Validation applied to an autoregressive model. https://cran.r-project.org/package=forecast |
| Gretl | fcast | Forecasting command with functionality for recursive-window forecasts. http://gretl.sourceforge.net/ | ||
| §2.6.1. Forecast combination: a brief review of statistical approaches | R | forecastHybrid | Functions for ensemble time series forecasts. https://cran.r-project.org/package=forecastHybrid | |
| §2.7.2. Forecasting on distributed systems | Database | InfluxDB | Scalable datastore for metrics, events, and real-time analytics. https://www.influxdata.com/time-series-database/ | |
| Database | OpenTSDB | A scalable, distributed Time Series Database. http://opentsdb.net/ | ||
| Database | RRDtool | A program for easily maintaining a database of time-series data. https://oss.oetiker.ch/rrdtool/ | ||
| Database | Timely | A time series database application that provides secure access to time series data. https://code.nsa.gov/timely/ | ||
| Python, R & Spark | darima | Implementations of distributed ARIMA models on Spark platform. https://github.com/xqnwang/darima | ||
| §2.7.3. Agent-based models | R | SpaDES | Spatially explicit discrete event simulation models. https://cran.r-project.org/package=SpaDES | |
| §2.7.4. Feature-based time series forecasting | R | tsfeatures | tsfeatures | Methods for extracting various features from time series data. https://cran.r-project.org/package=tsfeatures |
| Python | tsfresh | Calculates a large number of time series characteristics, the so called features. Further the package contains methods to evaluate the explaining power and importance of such characteristics for regression or classification tasks. https://tsfresh.readthedocs.io/en/latest/ | ||
| §2.7.4. Feature-based time series forecasting (continued) | Matlab | hctsa | Code framework that enables the extraction of thousands of time-series features from a time series (or a time-series dataset). It also provides a range of tools for visualising and analysing the resulting time-series feature matrix. https://github.com/benfulcher/hctsa | |
| Python | pyopy | Python binding for hctsa. https://github.com/strawlab/pyopy | ||
| R | fforma | Tools for forecasting using a model combination approach. It can be used for model averaging or model selection. It works by training a ‘classifier’ that learns to select/combine different forecast models. https://github.com/pmontman/fforma | ||
| R | gratis | Efficient algorithms for generating time series with diverse and controllable characteristics, which can be used as the training data in feature-based time series forecasting. https://cran.r-project.org/package=gratis | ||
| R | seer | Implementations of a novel framework for forecast model selection using time series features; FFORMS (Feature-based FORecast Model Selection). https://cran.r-project.org/package=seer | ||
| §2.7.5. Forecasting with bootstrap | R | bootstrap | Various bootstrapping functions. https://cran.r-project.org/package=bootstrap | |
| Gretl | uniFCextensions | uniFCboot | Estimate an interval forecast without assuming Gaussian innovations. http://gretl.sourceforge.net/ | |
| §2.7.6. Bagging for time series forecasting | R | forecast | baggedETS | Returns forecasts and other information for bagged ETS models. https://cran.r-project.org/package=forecast |
| R | tshacks | baggedClusterETS; treatedETS | Returns forecasts for bagged Cluster and Treated ETS models. https://github.com/tiagomendesdantas/tshacks | |
| §2.7.8. Neural Networks | Python, MXNet & PyTorch | GluonTS | Framework for building deep learning based models including a number of pre-built models such as feed-forward neural networks. https://github.com/awslabs/gluon-ts | |
| R | nnfor | mlp; elm | Time Series Forecasting with Neural Networks. https://cran.r-project.org/package=nnfor | |
| Python | neural prophet | Reimplementation of prophet in PyTorch, and extensions to it. https://github.com/ourownstory/neural_prophet | ||
| Python | RNNs for forecasting in Tensorflow. https://github.com/HansikaPH/time-series-forecasting | |||
| R | ANN2 | neuralnetwork | Artificial Neural Networks. https://cran.r-project.org/package=ANN2 | |
| R | nnet | nnet | Feed-Forward Neural Networks and Multinomial Log-Linear Models. https://cran.r-project.org/package=nnet | |
| R | forecast | nnetar | Auto-regressive Neural Network for time series forecasting. https://cran.r-project.org/package=forecast | |
| §2.7.9. Deep Probabilistic models | Python, MXNet & PyTorch | GluonTS | Framework for building deep learning based models including a number of pre-built models such as DeepAR, DeepState models and NBEATS. https://github.com/awslabs/gluon-ts | |
| Python, PyTorch | PyTorchTS | Clone of GluonTS in PyTorch. https://github.com/zalandoresearch/pytorch-ts | ||
| §2.7.10. Machine learning | R | RSNNS | mlp; rbf; dlvq; elman; jordan; som | Neural Networks using the Stuttgart Neural Network Simulator (SNNS). https://cran.r-project.org/package=RSNNS |
| §2.7.10. Machine learning (continued) | R | rpart | rpart; prune | Recursive partitioning and regression trees. https://cran.r-project.org/package=rpart |
| R | caret | Classification and regression training. https://cran.r-project.org/package=caret | ||
| R | e1071 | svm | Misc ML functions of the Department of Statistics, Probability Theory Group. https://cran.r-project.org/package=e1071 | |
| R | kernlab | gausspr | Gaussian processes for regression and classification. https://cran.r-project.org/package=kernlab | |
| R | brnn | brnn | Bayesian Regularisation for Feed-Forward Neural Networks. https://cran.r-project.org/package=brnn | |
| R | grnn | grnn | General regression neural network. https://cran.r-project.org/package=grnn | |
| R | randomForest | randomForest | Breiman and Cutler’s Random Forests for Classification and Regression. https://cran.r-project.org/package=randomForest | |
| R | gbm | gbm | Generalised Boosted regression models. https://cran.r-project.org/package=gbm | |
| R | neuralnet | neuralnet | Training of simple Neural Networks. https://cran.r-project.org/package=neuralnet | |
| Python | Tensorflow | A framework, developed by Google, offering tools for designing, building, and deploying ML models. https://tensorflow.org/api_docs/python/tf | ||
| Python | Keras API | A deep learning API built on top of Tensorflow. It provides high level blocks for building and training NN models. https://keras.io/ | ||
| R | Tensorflow | R Interface to Tensorflow (https://tensorflow.rstudio.com/). https://tensorflow.rstudio.com/ | ||
| R | deepnet | Deep learning toolkit. https://cran.r-project.org/package=deepnet | ||
| R | h2o | R Interface for the ‘H2O’ Scalable Machine Learning Platform. https://cran.r-project.org/package=h2o | ||
| R | Apache MXNet | A flexible library for deep learning. https://mxnet.apache.org/ | ||
| Python | scikit-learn | Ordinary Least Squares, Ridge regression, Lasso, Bayesian Regression, Generalized Linear Regression, Stochastic Gradient Descent and Polynomial regression, Support Vector Machines, Nearest Neighbors, Gaussian Processes, Decision Trees, Ensemble methods (Forests of randomised trees, AdaBoost and Gradient Tree Boosting), Multi-layer Perceptrons. https://scikit-learn.org/stable/ | ||
| Python | CNTK | A framework, developed by Microsoft, that provides tools for building ML and DL models. https://docs.microsoft.com/en-us/cognitive-toolkit/ | ||
| Python | PyTorch | A framework, developed by Facebook, for building ML and DL models. https://pytorch.org/ | ||
| §2.7.12. Clustering-based forecasting | R | tsfknn | tsfknn | Time Series Forecasting Using Nearest Neighbours. https://cran.r-project.org/package=tsfknn |
| §2.7.13. Hybrid methods | Python | ESRNN-GPU | A GPU-enabled version of the hybrid model used by the winner of M4 competition. https://github.com/damitkwr/ESRNN-GPU | |
| §2.8.1. Parametric methods for intermittent demand forecasting | R | tsintermittent | crost; tsb | Parametric forecasting methods for intermitternt demand. https://cran.r-project.org/package=tsintermittent |
| R | forecast | croston | Forecasts for intermittent demand using Croston’s method. https://cran.r-project.org/package=forecast | |
| §2.8.2. Non-parametric intermittent demand methods | R | tsintermittent | imapa | MAPA for intermittent demand data. https://cran.r-project.org/package=tsintermittent |
| §2.8.3. Classification methods | R | tsintermittent | idclass | Time series categorisation for intermittent demand. https://cran.r-project.org/package=tsintermittent |
| R | tsutils | abc; xyz; abcxyz | Classification functions and routines. https://cran.r-project.org/package=tsutils | |
| §2.9.3. Forecasting with text information | Python | NLTK | The Natural Language Toolkit in Python. https://www.nltk.org/ | |
| Python | SpaCy | An open source library for advanced Natural Language Processing in Python. https://spacy.io/ | ||
| §2.10.1. Cross-sectional hierarchical forecasting | R | hts | Functions and routines for hierarchical and grouped time series forecasting. https://cran.r-project.org/package=hts | |
| §2.10.2. Temporal aggregation | R | MAPA | mapa; mapasimple | Functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. https://cran.r-project.org/package=MAPA |
| R | thief | thief | Temporal Hierarchical Forecasting. https://cran.r-project.org/package=thief | |
| R | tsintermittent | imapa | MAPA for intermittent demand data with automatic model selection based on the PK classification. https://cran.r-project.org/package=tsintermittent | |
| §2.10.4. Ecological inference forecasting | R | ei | ei | Returns local and global forecasts of inner cells in \(2 \times 2\) tables. https://cran.r-project.org/package=ei |
| R | eiPack | ei.MD.bayes; ei.reg; ei.reg.bayes | Returns local and global forecasts of inner cells in R\(\times\)C tables under a Multinomial Dirichlet model or using ecological regression. https://cran.r-project.org/package=eiPack | |
| R | lphom | lphom; tslphom; nslphom | Returns forecasts of inner cells of a R\(\times\)C table using linear programming optimisation. https://CRAN.R-project.org/package=lphom | |
| R | eiCompare | ei_est_gen; ei_good; ei_rxc | Returns forecasts of inner cells of a \(R \times C\) tables using iterative versions of \(2 \times 2\) methods and the Multinomial Dirichlet model. https://cran.r-project.org/package=eiCompare | |
| §2.12.2. Point, interval, and pHDR forecast error measures | R | forecast | accuracy | Accuracy measures for a forecast model. https://cran.r-project.org/package=forecast |
| §2.12.4. Evaluating probabilistic forecasts | R | scoringRules | Scoring rules for parametric and simulated distribution forecasts. https://cran.r-project.org/package=scoringRules | |
| R | verification | crps | Continuous ranked probability score. https://cran.r-project.org/package=verification | |
| §2.12.6. Statistical tests of forecast performance | R | forecast | dm.test | Diebold-Mariano test for predictive accuracy. https://cran.r-project.org/package=forecast |
| R | tsutils | nemenyi | Nonparametric multiple comparisons (Nemenyi test). https://cran.r-project.org/package=tsutils | |
| §2.12.6. Statistical tests of forecast performance (continued) | Gretl | FEP | doMZtest; doHPtest; doEKTtest; doPTtest; doDLtest; doDMtest; doGWtest; doCWtest | Various statistical tests on forecast unbiasedness, efficiency, asymmetric loss and directional changes. http://gretl.sourceforge.net/ |
| Gretl | DiebMar | Diebold-Mariano test. http://gretl.sourceforge.net/ |