- Financial Time Series Data
- Exploring Time Series Data in R
- Plotting Time Series in R
- Handling Missing Values in Time Series
- Creating a Time Series Object in R
- Check if an object is a time series object in R
- Plotting Financial Time Series Data (Multiple Columns) in R
- Characteristics of Time Series
- Stationary Process in Time Series
- Transforming a Series to Stationary
- Time Series Transformation in R
- Differencing and Log Transformation
- Autocorrelation in R
- Time Series Models
- ARIMA Modeling
- Simulate White Noise (WN) in R
- Simulate Random Walk (RW) in R
- AutoRegressive (AR) Model in R
- Estimating AutoRegressive (AR) Model in R
- Forecasting with AutoRegressive (AR) Model in R
- Moving Average (MA) Model in R
- Estimating Moving Average (MA) Model in R
- ARIMA Modelling in R
- ARIMA Modelling - Identify Model for a Time Series
- Forecasting with ARIMA Modeling in R - Case Study
- Automatic Identification of Model Using auto.arima() Function in R
- Financial Time Series in R - Course Conclusion

# Automatic Identification of Model Using auto.arima() Function in R

### auto.arima() Function

R also has a package called forecast, which contains many forecasting functions for time series and linear models. It also contains a very useful function called `auto.arima`

, which returns the best ARIMA model according to either AIC, AICc or BIC value. The function conducts a search over possible models within the order constraints provided.

Let's try the `auto.arima`

function on our time series.

```
> fb\_fit\_auto <- auto.arima(fb\_ts)
> fb\_fit\_auto
Series: fb\_ts
ARIMA(1,1,4) with drift
Coefficients:
ar1 ma1 ma2 ma3 ma4 drift
-0.8405 0.8626 -0.0123 -0.1632 -0.1768 0.0800
s.e. 0.1080 0.1111 0.0488 0.0509 0.0381 0.0478
sigma^2 estimated as 2.581: log likelihood=-1426.29
AIC=2866.58 AICc=2866.73 BIC=2898.97
>
```

The `auto.arima`

function suggests the best fit model as ARIMA(1,1,4) with drift.

The following code creates the forecast for the FB stock prices using the suggested model:

```
fb\_fit <- arima(fb\_ts, order = c(1, 1, 4))
fb\_forecast <- predict(fb\_fit , n.ahead = 20)
fb\_forecast\_values <- fb\_forecast$pred
plot.ts(fb\_ts, xlim = c(0, 900), ylim = c(50,160))
points(fb\_forecast\_values , type = "l", col = 2)
```

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