InterNL: Time Series Intervention Model Using Non-linear Function

In InterNL: Time Series Intervention Model Using Non-Linear Function

Time Series Intervention Model Using Non-linear Function

Description

Time Series Intervention Model Using Non-linear Function

Usage

InterNL(Data, Time, TSModel, TSOrder = NULL, NLModel, InitialNLM)

Arguments

Data	Time series data
Time	Point of intervention
TSModel	Time series model ("arima" or "ann")
TSOrder	If model is ANN, then order is lag of the model
NLModel	Non-linear models ("gompertz","logistic", "monomolecular", "richard", "hoerl")
InitialNLM	Initial value for parameters of non-linear model

Value

• Accuracy: Accuracy metric of the proposed model

• PreFitted: Fitted values for the pre intervention series

• PostFitted: Prediction for the post intervention series

• NLM: Details of fitted non-linear model

References

• Paul, R.K. and Yeasin, M., 2022. COVID-19 and prices of pulses in Major markets of India: Impact of nationwide lockdown. Plos one, 17(8), p.e0272999.

• Yeasin, M., Paul, R.K., Das, S., Deka, D. and Karak, T., 2023. Change in the air due to the coronavirus outbreak in four major cities of India: What do the statistics say?. Journal of Hazardous Materials Advances, 10, p.100325.

Examples

library("InterNL")
data<- as.ts(rnorm(120,100,50))
Result <- InterNL(Data = data,Time = 90, TSModel = "arima",
TSOrder=NULL, NLModel=NULL, InitialNLM=NULL )

InterNL documentation built on May 29, 2024, 8:57 a.m.