| Type: | Package |
| Title: | Time Series Prediction with PSF and Decomposition Methods (EMD and EEMD) |
| Version: | 0.2 |
| Date: | 2022-04-30 |
| Author: | Neeraj Bokde |
| Maintainer: | Neeraj Bokde <neerajdhanraj@gmail.com> |
| URL: | https://www.neerajbokde.in/software/ |
| Description: | 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. |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| Imports: | PSF, Rlibeemd, forecast, tseries |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.2 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2022-05-01 03:52:20 UTC; mayur |
| Repository: | CRAN |
| Date/Publication: | 2022-05-01 14:20:07 UTC |
Function to predict with EEMD-ARIMA model
Description
Function to predict with EEMD-ARIMA model
Usage
eemdarima(data, n.ahead)
Arguments
data |
as input time series data |
n.ahead |
as horizon of values to be predicted |
Value
predicted values with EEMD-ARIMA model
Examples
# eemdarima(data = nottem, n.ahead = 6)
Function to predict with EEMD-PSF model
Description
Function to predict with EEMD-PSF model
Usage
eemdpsf(data, n.ahead)
Arguments
data |
as input time series data |
n.ahead |
as horizon of values to be predicted |
Value
predicted values with EEMD-PSF model
Examples
# eemdpsf(data = nottem, n.ahead = 6)
Function to predict with EEMD-PSF,ARIMA model
Description
Function to predict with EEMD-PSF,ARIMA model
Usage
eemdpsfarima(data, n.ahead)
Arguments
data |
as input time series data |
n.ahead |
as horizon of values to be predicted |
Value
predicted values with EEMD-PSF,ARIMA model
Examples
# eemdpsfarima(data = nottem, n.ahead = 6)
Function to predict with EMD-ARIMA model
Description
Function to predict with EMD-ARIMA model
Usage
emdarima(data, n.ahead)
Arguments
data |
as input time series data |
n.ahead |
as horizon of values to be predicted |
Value
predicted values with EMD-ARIMA model
Examples
# emdarima(data = nottem, n.ahead = 6)
Function to predict with EMD-PSF model
Description
Function to predict with EMD-PSF model
Usage
emdpsf(data, n.ahead)
Arguments
data |
as input time series data |
n.ahead |
as horizon of values to be predicted |
Value
predicted values with EMD-PSF model
Examples
# emdpsf(data = nottem, n.ahead = 6)
Function to predict with EMD-PSF,ARIMA model
Description
Function to predict with EMD-PSF,ARIMA model
Usage
emdpsfarima(data, n.ahead)
Arguments
data |
as input time series data |
n.ahead |
as horizon of values to be predicted |
Value
predicted values with EMD-PSF,ARIMA model
Examples
# emdpsfarima(data = nottem, n.ahead = 6)
Function to restrict the legth of dataset in multiples of 24
Description
Function to restrict the legth of dataset in multiples of 24
Usage
lpsf(data, n.ahead)
Arguments
data |
as inpute time series |
n.ahead |
as horizon of values to be predicted |
Value
returns the predictied results