# Dynamic Regression (ARIMA) vs. XGBoost

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In the previous article, we mentioned that we were going to compare dynamic regression with ARIMA errors and the xgboost. Before doing that, let’s talk about dynamic regression.

Time series modeling, most of the time, uses past observations as predictor variables. But sometimes, we need external variables that affect the target variables. To include those variables, we have to use regression models. However, we are going to use **dynamic regression** to capture elaborated patterns; the difference from the orthodox regression models is that residuals are not white noise and are modeled by** ARIMA**.

The residuals we mentioned above, have autocorrelation, which means contain information. To indicate that, we will show as . In this way, the residuals term can be modeled by ARIMA. For instance, dynamic regression with ARIMA(1,1,1) as described:

denotes the white noise and **B**, the backshift notation. As we can see above equation, There two error terms: the one from regression model, , and the other from ARIMA model, .

In the previous article, we have created the dataset variable,* df_xautry*. We will transform it into the multivariate time series and split it as a test and training set. Finally, we will model the training data.

library(dplyr) library(forecast) #Building the multivariate time series df <- df_xautry[-1] df_mts <- df %>% ts(start=c(2013,1),frequency=12) #Split the dataset train <- df_mts %>% window(end=c(2020,12)) test <- df_mts %>% window(start=2021) #Modeling the training data fit_dynamic <- auto.arima(train[,"xau_try_gram"], xreg =train[,c(1,2)]) #Series: train[, "xau_try_gram"] #Regression with ARIMA(1,0,2) errors #Coefficients: # ar1 ma1 ma2 intercept xe xau_usd_ounce # 0.9598 -0.0481 0.4003 -150.8309 43.8402 0.1195 #s.e. 0.0390 0.0992 0.1091 23.7781 2.1198 0.0092 #sigma^2 estimated as 27.15: log likelihood=-293.31 #AIC=600.62 AICc=601.89 BIC=618.57

Based on the above results, we have ARIMA(1,0,2) model as described below:

Now, we will do forecasting and then calculate accuracy. The accuracy for xgboost will be calculated from the *forecast_xautrygram* variable.

#Forecasting fcast_dynamic <- forecast(fit_dynamic, xreg = test[,1:2]) #Accuracy acc_dynamic <- fcast_dynamic %>% accuracy(test[,3]) %>% .[,c("RMSE","MAPE")] acc_xgboost <- forecast_xautrygram %>% accuracy(test[,3]) %>% .[,c("RMSE","MAPE")]

In order to visualize the accuracy results, we’re going to build the data frame and prepare it for a suitable bar chart.

#Tidying the dataframe df_comparison <- data.frame( "dynamic"=acc_dynamic, "xgboost"=acc_xgboost ) df_comparison # dynamic.RMSE dynamic.MAPE xgboost.RMSE xgboost.MAPE #Training set 5.044961 2.251683 0.001594868 0.000805107 #Test set 10.695489 2.501123 11.038134819 2.060825426 library(tidyr) df_comparison %>% rownames_to_column(var = "data") %>% gather(`dynamic.RMSE`, `dynamic.MAPE`,`xgboost.RMSE`, `xgboost.MAPE`, key = "models", value="score") -> acc_comparison acc_comparison # data models score #1 Training set dynamic.RMSE 5.044960948 #2 Test set dynamic.RMSE 10.695489161 #3 Training set dynamic.MAPE 2.251682989 #4 Test set dynamic.MAPE 2.501122965 #5 Training set xgboost.RMSE 0.001594868 #6 Test set xgboost.RMSE 11.038134819 #7 Training set xgboost.MAPE 0.000805107 #8 Test set xgboost.MAPE 2.060825426 #Plotting comparing models ggplot(acc_comparison,aes(x=data,y=score,fill = models)) + geom_bar(stat = "identity",position = "dodge") + theme_bw()

**Conclusion**

When we examined the above results and the bar chart for unseen data, we are seeing some interesting results. For the training set, the xgboost model has near-zero accuracy rates which can lead to overfitting. The dynamic model looks slightly better for the RMSE but vice versa for the MAPE criteria.

This shed light that while the first-month dynamic model is better, for the second month the xgboost looks closer to the actual observation. Of course, the reason for that is maybe we have few test data but I wanted to predict the first two months of the current year. Maybe next time, if we have more test data, we can try again.

**References**

- Forecasting: Principles and Practice,
*Rob J Hyndman and George Athanasopoulos*

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