predtoolsTS: R package for streamlining time series forecasting

Charte, Francisco; Vico, A.; Pérez-Godoy, María Dolores; Rivera, Antonio J.

doi:10.1007/s13748-019-00193-z

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Following several established research works from the literature, proposing a new softcomputing methodology must be validated with real time series datasets [39][40][41][42]. The proposed package in the current research was examined on six different time series dataset.…”

Section: Demonstrationmentioning

confidence: 99%

Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package

et al. 2022

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Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced.

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Section: Demonstrationmentioning

confidence: 99%

Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package

et al. 2022

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“…Recently, a package in R was proposed for streamlining time series forecasting with limited facilities and features, named as predtoolsTS [43,44]. This tool assists in forecasting with the automated Auto-Regressive Integrated Moving Average (ARIMA) model [38] and only regression type algorithms from the caret [45] package in the closed environment.…”

Section: Overview Of Forecasttbmentioning

confidence: 99%

ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling

Bokde

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Andersen

2020

Energies

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This paper introduces an R package ForecastTB that can be used to compare the accuracy of different forecasting methods as related to the characteristics of a time series dataset. The ForecastTB is a plug-and-play structured module, and several forecasting methods can be included with simple instructions. The proposed test-bench is not limited to the default forecasting and error metric functions, and users are able to append, remove, or choose the desired methods as per requirements. Besides, several plotting functions and statistical performance metrics are provided to visualize the comparative performance and accuracy of different forecasting methods. Furthermore, this paper presents real application examples with natural time series datasets (i.e., wind speed and solar radiation) to exhibit the features of the ForecastTB package to evaluate forecasting comparison analysis as affected by the characteristics of a dataset. Modeling results indicated the applicability and robustness of the proposed R package ForecastTB for time series forecasting.

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