| Title: | Intuitionistic Fuzzy Autoregressive Integrated Moving Average Model |
|---|---|
| Description: | The Intuitionistic Fuzzy Autoregressive Integrated Moving Average model offers advanced time series forecasting by integrating intuitionistic fuzzy sets with ARIMA. It enhances prediction accuracy by handling uncertainties and hesitations more effectively than traditional models, making it ideal for complex, real-world data applications. The package has been developed using the algorithm of Yolcu and Yolcu (2023) <doi:10.1016/j.eswa.2022.119336>. |
| Authors: | Anita Sarkar [aut], Ankit Kumar Singh [aut], Md Yeasin [aut, cre], Ranjit Kumar Paul [aut], Amrit Kumar Paul [aut], Pushkar Bora [aut] |
| Maintainer: | Md Yeasin <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2026-10-29 17:11:15 UTC |
| Source: | https://github.com/cran/IFARIMA |
Implements an intuitionistic fuzzy ARIMA forecasting model by integrating fuzzy clustering information with ARIMA modelling.
IFARIMA(Data, Class, Split)IFARIMA(Data, Class, Split)
Data |
Numeric time series data (a numeric vector without missing values). |
Class |
Number of fuzzy classes (at least 2). |
Split |
Proportion of the data used for training, strictly between 0 and 1. |
The membership matrix of the intuitionistic fuzzy clustering step is
initialised at random, so results depend on the state of the random number
generator. Call set.seed() before IFARIMA() to obtain
reproducible results. The first round(length(Data) * Split)
observations form the training set and the remainder the test set.
The membership degrees of an observation sum to one and the non-membership degrees are one minus the memberships, so some of these regressors are linearly dependent. Redundant columns are removed automatically before the ARIMA model with external regressors is fitted.
A list with three components:
AccuracyAccuracy measures (as returned by
forecast::accuracy) for the training and test sets.
Train_FittedNumeric vector of fitted values on the training set.
Test_PredictionNumeric vector of forecasts for the test set.
Yolcu, O.C. and Yolcu, U. (2023). A novel intuitionistic fuzzy time series prediction model with cascaded structure for financial time series. Expert Systems with Applications, 215, 119336. doi:10.1016/j.eswa.2022.119336
# Small toy example (runs in about a second) set.seed(123) dat <- 50 + cumsum(rnorm(60)) Model <- IFARIMA(Data = dat, Class = 2, Split = 0.8) Model$Accuracy head(Model$Train_Fitted) Model$Test_Prediction # Larger example with three clusters set.seed(123) dat2 <- rnorm(100, 60, 10) Model2 <- IFARIMA(Data = dat2, Class = 3, Split = 0.8) Model2$Accuracy# Small toy example (runs in about a second) set.seed(123) dat <- 50 + cumsum(rnorm(60)) Model <- IFARIMA(Data = dat, Class = 2, Split = 0.8) Model$Accuracy head(Model$Train_Fitted) Model$Test_Prediction # Larger example with three clusters set.seed(123) dat2 <- rnorm(100, 60, 10) Model2 <- IFARIMA(Data = dat2, Class = 3, Split = 0.8) Model2$Accuracy