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auro_arima_pb.Rmd
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auro_arima_pb.Rmd
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---
title: "Auto-arima test for RPL"
output: html_notebook
---
#R indexing--> [], starts from 1
#Python indexing--> [), starts from 0
## Objective: forecast 90 days after the latest data
```{r}
library(forecast)
library(Metrics)
library(zoo)
```
```{r}
data = read.csv("inflation_actual_f.csv")
```
```{r}
pacf(ts(data[,1]),1,lag.max = 90)
```
## FORECAST
```{r}
myp=7
myq=7
maxorder<-(myp+myq)*2
#model = auto.arima(as.vector(train['Weekly_Recharges']),stepwise=FALSE,approximation = FALSE, max.p = 8, max.q = 16, max.order=maxorder, num.cores = 6)
model = auto.arima(ts(rev(data$inflation),frequency = 1),seasonal = FALSE,stepwise=FALSE,approximation = TRUE, max.p = myp, max.q = myq, max.P = myp, max.Q = myq, max.d = 4, max.D=4, max.order=120, num.cores = 3,parallel = TRUE)
```
```{r}
arimaorder(model)
```
```{r}
flength=6
fcast_no_holdout <- forecast(model,flength)
```
```{r}
plot(fcast_no_holdout)
```
```{r}
out=as.data.frame(fcast_no_holdout) # data can be captured to target excel files
write.csv(out, file = "pb_forecast_b.csv",row.names=TRUE)
```
# INVEST!