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. The further technical details and references regarding PSF are discussed in Vignette.
Last updated
6.66 score 17 stars 1 dependents 36 scripts 311 downloadsVedicDateTime - Vedic Calendar System
Provides platform for Vedic calendar system having several functionalities to facilitate conversion between Gregorian and Vedic calendar systems, and helpful in examining its impact in the time series analysis domain.
Last updated
calendarpanchangatime-seriesvedic
5.99 score 8 stars 61 scripts 261 downloadsimputeTestbench - Test Bench for the Comparison of Imputation Methods
Provides a test bench for the comparison of missing data imputation methods in uni-variate time series. Imputation methods are compared using different error metrics. Proposed imputation methods and alternative error metrics can be used.
Last updated
5.06 score 6 stars 1 dependents 27 scripts 4.8k downloadsdecomposedPSF - Time Series Prediction with PSF and Decomposition Methods (EMD and EEMD)
Predict future values with hybrid combinations of Pattern Sequence based Forecasting (PSF), Autoregressive Integrated Moving Average (ARIMA), Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) methods based hybrid methods.
Last updated
2.08 score 12 scripts 211 downloads