Software and Tools

R Packages

1. PSF: Forecasting of Univariate Time Series Using the Pattern Sequence-Based Forecasting (PSF) Algorithm

  • Pattern Sequence Based Forecasting (PSF) takes univariate time series data as input and assist to forecast its future values.
  • This algorithm forecasts the behavior of time series based on similarity of pattern sequences.
  • Initially, clustering is done with the labeling of samples from database.
  • The labels associated with samples are then used for forecasting the future behaviour of time series data.

w1

CRAN


2. imputeTestbench: Test Bench for the Comparison of Imputation Methods


3. ForecastTB: Test Bench for the Comparison of Forecast Methods

  • Provides a test bench for the comparison of forecasting methods in uni-variate time series.
  • Forecasting methods are compared using different error metrics.
  • Proposed forecasting methods and alternative error metrics can be used.

w3

CRAN

Please click on three parallel lines on left-top of the Shiny panel for better visualization.

Publication/Manual


4. CleanTS: Testbench for Univariate Time Series Cleaning

  • A reliable and efficient tool for cleaning univariate time series data.
  • It implements reliable and efficient procedures for automating the process of cleaning univariate time series data.
  • The package provides integration with already developed and deployed tools for missing value imputation and outlier detection.
  • It also provides a way of visualizing large time-series data in different resolutions.

w4

CRAN

Please click on three parallel lines on left-top of the Shiny panel for better visualization.

Publication/Manual

  • Shende M., Feijoo A., and Bokde N. (2022). cleanTS: Automated (AutoML) Tool to Clean Univariate Time Series at Microscales. Neurocomputing (IF 5.719). 500, 155-176.. DOI: 10.1016/j.neucom.2022.05.057

5. GuessCompx: Empirically Estimates Algorithm Complexity


6. Jaya: a Gradient-Free Optimization Algorithm

  • Maximization or Minimization of a fitness function using Jaya Algorithm (JA).
  • A population based method which repeatedly modifies a population of individual solutions.
  • Capable of solving both constrained and unconstrained optimization problems.
  • It does not contain any hyperparameters.
  • For further details: R.V. Rao (2016) <doi:10.5267/j.ijiec.2015.8.004> . w6a w6b

    CRAN

  • https://cran.r-project.org/package=Jaya

    Publication/Manual

  • Bokde N., and Shende M. (2020). A guide to Jaya Package.

7. WindCurves: Tool to Fit Wind Turbine Power Curves

  • Provides a tool to fit and compare the wind turbine power curves with successful curve fitting techniques.
  • Facilitates to examine and compare the performance of a user-defined power curve fitting techniques.
  • Also, provide features to generate power curve discrete points from a graphical power curves.
  • Data on the power curves of the wind turbine from major manufacturers are provided. w7

    CRAN

  • https://cran.r-project.org/package=WindCurves

    Publication/Manual

  • Bokde N., Feijoo A., and Villanueva D. (2018). Wind turbine power curves based on Weibull cumulative distribution function. Applied Sciences (Invited/Feature paper) (IF 2.679), 8(10), 1757.

8. decomposedPSF: Time Series Prediction with PSF and Decomposition Methods (EMD and EEMD)


9. imputePSF: Impute Missing Data in Time Series Data with PSF Based Method