| Type: | Package |
| Title: | Hybrid ARIMA-GARCH and Two Specially Designed ML-Based Models |
| Version: | 0.1.0 |
| Author: | Mr. Sandip Garai [aut, cre] |
| Maintainer: | Mr. Sandip Garai <sandipnicksandy@gmail.com> |
| Description: | Describes a series first. After that does time series analysis using one hybrid model and two specially structured Machine Learning (ML) (Artificial Neural Network or ANN and Support Vector Regression or SVR) models. More information can be obtained from Paul and Garai (2022) <doi:10.1007/s41096-022-00128-3>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | AllMetrics, DescribeDF, stats, dplyr, psych, FinTS, tseries, forecast, fGarch, aTSA, neuralnet, e1071 |
| RoxygenNote: | 7.2.1 |
| NeedsCompilation: | no |
| Packaged: | 2023-04-12 11:54:55 UTC; user |
| Repository: | CRAN |
| Date/Publication: | 2023-04-13 09:10:06 UTC |
ARIMA-GARCH Hybrid Modeling
Description
First fits the time series data by using ARIMA model. If the residuals are having "arch" effect, then GARCH is fitted. Based on the previously mentioned condition final prediction is obtained.
Usage
ariga(Y, ratio = 0.9, n_lag = 4)
Arguments
Y |
Univariate time series |
ratio |
Ratio of number of observations in training and testing sets |
n_lag |
Lag of the provided time series data |
Value
Output_ariga: List of three data frames containing three data frames namely predict_compare, forecast_compare, and metrics
References
Paul, R. K., & Garai, S. (2021). Performance comparison of wavelets-based machine learning technique for forecasting agricultural commodity prices. Soft Computing, 25(20), 12857-12873.
Paul, R. K., & Garai, S. (2022). Wavelets based artificial neural network technique for forecasting agricultural prices. Journal of the Indian Society for Probability and Statistics, 23(1), 47-61.
Garai, S., Paul, R. K., Rakshit, D., Yeasin, M., Paul, A. K., Roy, H. S., Barman, S. & Manjunatha, B. (2023). An MRA Based MLR Model for Forecasting Indian Annual Rainfall Using Large Scale Climate Indices. International Journal of Environment and Climate Change, 13(5), 137-150.
Examples
Y <- rnorm(100, 100, 10)
result <- ariga(Y, ratio = 0.8, n_lag = 4)
Specially Designed ANN-Based Modeling
Description
Fits a specially designed ANN model to the uni-variate time series data. The contribution is related to the PhD work of the maintainer.
Usage
my_ann(Y, ratio = 0.9, n_lag = 4)
Arguments
Y |
Univariate time series |
ratio |
Ratio of number of observations in training and testing sets |
n_lag |
Lag of the provided time series data |
Value
Output_ann: List of three data frames containing three data frames namely predict_compare, forecast_compare, and metrics
References
Paul, R. K., & Garai, S. (2021). Performance comparison of wavelets-based machine learning technique for forecasting agricultural commodity prices. Soft Computing, 25(20), 12857-12873.
Paul, R. K., & Garai, S. (2022). Wavelets based artificial neural network technique for forecasting agricultural prices. Journal of the Indian Society for Probability and Statistics, 23(1), 47-61.
Garai, S., Paul, R. K., Rakshit, D., Yeasin, M., Paul, A. K., Roy, H. S., Barman, S. & Manjunatha, B. (2023). An MRA Based MLR Model for Forecasting Indian Annual Rainfall Using Large Scale Climate Indices. International Journal of Environment and Climate Change, 13(5), 137-150.
Examples
Y <- rnorm(100, 100, 10)
result <- my_ann(Y, ratio = 0.8, n_lag = 4)
Specially Designed SVR-Based Modeling
Description
Fits a specially designed SVR model to the uni-variate time series data. The contribution is related to the PhD work of the maintainer.
Usage
my_svr(Y, ratio = 0.9, n_lag = 4)
Arguments
Y |
Univariate time series |
ratio |
Ratio of number of observations in training and testing sets |
n_lag |
Lag of the provided time series data |
Value
Output_svr: List of three data frames containing three data frames namely predict_compare, forecast_compare, and metrics
References
Paul, R. K., & Garai, S. (2021). Performance comparison of wavelets-based machine learning technique for forecasting agricultural commodity prices. Soft Computing, 25(20), 12857-12873.
Paul, R. K., & Garai, S. (2022). Wavelets based artificial neural network technique for forecasting agricultural prices. Journal of the Indian Society for Probability and Statistics, 23(1), 47-61.
Garai, S., Paul, R. K., Rakshit, D., Yeasin, M., Paul, A. K., Roy, H. S., Barman, S. & Manjunatha, B. (2023). An MRA Based MLR Model for Forecasting Indian svrual Rainfall Using Large Scale Climate Indices. International Journal of Environment and Climate Change, 13(5), 137-150.
Examples
Y <- rnorm(100, 100, 10)
result <- my_svr(Y, ratio = 0.8, n_lag = 4)
Descriptive Statistics Of A Series
Description
Provides descriptive statistics of a particular series. First column in the output result mentions 10 different statistics and second column contains the Statistics values of the particular series.
Usage
series_descstat(Y)
Arguments
Y |
Univariate time series |
Value
desc_table: A table contains 10 descriptive statistics row-wise
References
Garai, S., & Paul, R. K. (2023). Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence. Intelligent Systems with Applications, 18, 200202.
Garai, S., Paul, R. K., Rakshit, D., Yeasin, M., Paul, A. K., Roy, H. S., Barman, S. & Manjunatha, B. (2023). An MRA Based MLR Model for Forecasting Indian svrual Rainfall Using Large Scale Climate Indices. International Journal of Environment and Climate Change, 13(5), 137-150.
Examples
Y <- rnorm(100, 100, 10)
result <- series_descstat(Y)
Non linearity test of a Data Frame
Description
Performs non linearity test result for a series. Provides output as a single element (data frame) list. First column mentions different statistics (eps). Other columns are the Statistics values of the particular dimension.
Usage
series_nonlinearity(Y)
Arguments
Y |
Univariate time series |
Value
nonlinearity_list: A list with a single element (data frame) . Element is named as the name of the series provided. The element is such that first column mentions different statistics and other columns are the Statistics values of the particular dimension.
References
Garai, S., & Paul, R. K. (2023). Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence. Intelligent Systems with Applications, 18, 200202.
Garai, S., Paul, R. K., Rakshit, D., Yeasin, M., Paul, A. K., Roy, H. S., Barman, S. & Manjunatha, B. (2023). An MRA Based MLR Model for Forecasting Indian svrual Rainfall Using Large Scale Climate Indices. International Journal of Environment and Climate Change, 13(5), 137-150.
Examples
my_series <- rnorm(100, 100, 10)
nonlinearity <- series_nonlinearity(my_series)
nonlinearity$my_series
Stationarity Tests Of A Series
Description
Provides a list of three data frames: 'ADF', 'PP', 'KPSS'. Also indicates whether the data is stationary or not according to the null hypothesis of the corresponding tests.
Usage
series_stationarity(Y)
Arguments
Y |
Univariate time series |
Value
stationarity_table - List of three data frames: 'ADF', 'PP', 'KPSS'
References
Garai, S., & Paul, R. K. (2023). Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence. Intelligent Systems with Applications, 18, 200202.
Garai, S., Paul, R. K., Rakshit, D., Yeasin, M., Paul, A. K., Roy, H. S., Barman, S. & Manjunatha, B. (2023). An MRA Based MLR Model for Forecasting Indian svrual Rainfall Using Large Scale Climate Indices. International Journal of Environment and Climate Change, 13(5), 137-150.
Examples
Y <- rnorm(100, 100, 10)
result <- series_stationarity(Y)