US Natural Gas Consumption

In this project I am going to forecast the US Natural Gas Consumption for the following 12 months. The data used for this forecast represents the monthly consumption of gas in the US between 2000 and 2019 (at the moment of this study the latest data is from July 2019). This is a dataset included in the R programming enviroment.

Loading the necessary libraries

library(forecast)
library(TSstudio)
library(plotly)
library(tidyverse)
library(TSstudio)
library(plotly)
library(stats)
library(forecast)
library(tidyverse)
library(dplyr)
library(ggplot2)
library(dygraphs)
library(lubridate)
library(datasets)
library(base)
library(h2o)
library(Quandl)
library(ggfortify)

Loading the data into R

data(USgas)
head(USgas, 40)
        Jan    Feb    Mar    Apr    May    Jun    Jul    Aug    Sep    Oct
2000 2510.5 2330.7 2050.6 1783.3 1632.9 1513.1 1525.6 1653.1 1475.0 1567.8
2001 2677.0 2309.5 2246.6 1807.2 1522.4 1444.4 1598.1 1669.2 1494.1 1649.1
2002 2487.6 2242.4 2258.4 1881.0 1611.5 1591.4 1748.4 1725.7 1542.2 1645.9
2003 2700.5 2500.3 2197.9 1743.5                                          
        Nov    Dec
2000 1908.5 2587.5
2001 1701.0 2120.2
2002 1913.6 2378.9
2003

Exploratory data analysis

ts_info(USgas)
##  The USgas series is a ts object with 1 variable and 235 observations
##  Frequency: 12 
##  Start time: 2000 1 
##  End time: 2019 7
autoplot(USgas) + ggtitle("Us Monthly Natural Gas Consumption") + 
        xlab("Year") + ylab("Billion Cubic Feet")

decompose(USgas) %>% plot()

Exploratory analysis conclusions

We can see that the series has an additive structure, has trend which seems fairly linear and a very notorious seasonal component.

Seasonality analysis

We first convert the time series into a data frame, because we will later use ggplots. We also create a year, month and day columns (this will come in handy during the analysis).

USgas_df <- ts_to_prophet(USgas)
USgas_df$year <- lubridate::year(USgas_df$ds)
USgas_df$month <- lubridate::month(USgas_df$ds)
USgas_df$day <- lubridate::day(USgas_df$ds)
USgas_df <- USgas_df[,c(1,3,4,5,2)]
names(USgas_df) <- c("date", "year", "month", "day", "USgas")

We can see there’s a seasonal component, because the density plots aren’t overlapping

ggplot(data = USgas_df, aes(x = USgas)) + geom_density(data = USgas_df, aes(fill = month)) + 
        ggtitle("USgas - Density plots by Month") + facet_grid(month ~.)

To analyse the real seasonal pattern, we detrend the series and plot the density plots for each month.

USgas_df$USgas_detrend <- USgas_df$USgas - decompose(USgas)$trend
ggplot(data = USgas_df, aes(x = USgas_detrend)) + geom_density(data = USgas_df, aes(fill = month)) +
        ggtitle("USgas - Density plots by Month (detrended series)") + facet_grid(month ~.)

ts_seasonal(USgas, type = "all")

Seasonality conclusions

Correlation analysis

par(mfrow = c(1,2))
acf(USgas, lag.max = 60)
pacf(USgas, lag.max = 60)

ts_lags(USgas)

Correlation conclusions

Creating the training and testing partitions

USgas_split <- ts_split(USgas, sample.out = 12)
train <- USgas_split$train
test <- USgas_split$test

Forecasting approaches

Multivariate Linear Regression

1. Linear Regression (trend & season)

tslm_1 <- tslm(train ~ season + trend)
summary(tslm_1)

Call:
tslm(formula = train ~ season + trend)

Residuals:
    Min      1Q  Median      3Q     Max 
-515.91  -72.13  -13.97   85.37  328.98 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2496.5284    32.1414  77.673  < 2e-16 ***
season2      -299.3562    40.7466  -7.347 4.43e-12 ***
season3      -482.8861    40.7472 -11.851  < 2e-16 ***
season4      -911.6475    40.7483 -22.373  < 2e-16 ***
season5     -1096.5247    40.7497 -26.909  < 2e-16 ***
season6     -1116.5283    40.7516 -27.398  < 2e-16 ***
season7      -952.9319    40.7539 -23.383  < 2e-16 ***
season8      -938.9307    41.3086 -22.730  < 2e-16 ***
season9     -1145.0440    41.3093 -27.719  < 2e-16 ***
season10    -1056.8072    41.3103 -25.582  < 2e-16 ***
season11     -806.0092    41.3117 -19.510  < 2e-16 ***
season12     -268.3669    41.3136  -6.496 5.88e-10 ***
trend           2.5299     0.1307  19.357  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 125.6 on 210 degrees of freedom
Multiple R-squared:  0.9178,    Adjusted R-squared:  0.9131 
F-statistic: 195.3 on 12 and 210 DF,  p-value: < 2.2e-16

Since all the variables are statistically significant we proceed to train the model.

fc_tslm1 <- forecast(tslm_1, h = 12)
accuracy(fc_tslm1, test)
                       ME     RMSE       MAE        MPE     MAPE      MASE
Training set 9.819624e-16 121.8732  94.22214 -0.3115099 4.652981 0.8313625
Test set     2.156608e+02 238.8566 215.66083  8.4045062 8.404506 1.9028685
                     ACF1 Theil's U
Training set  0.578957894        NA
Test set     -0.004188979 0.8104123

The model seems to be overfitting since it obtains a higher result in all the error metrics in the testing partition compared to the training set.

test_forecast(actual = USgas,
              forecast.obj = fc_tslm1,
              test = test)
checkresiduals(tslm_1)


    Breusch-Godfrey test for serial correlation of order up to 24

data:  Residuals from Linear regression model
LM test = 108.48, df = 24, p-value = 1.037e-12

Conclusions about first linear regression model (modeling with trend and season components)

  • All the variables are statistically significant (they’re not independent).
  • Residuals are not white noise (the model didn’t capture all the patterns).
  • Residuals are correlated with their lags and they are fairly normally distributed.

2. Linear Regression (trend, season & seasonal lag)

First we have to transform the time series into a dataframe, in order to create a new variable (lag 12), which represents the first seasonal lag of the series.

USgas_df_2 <- USgas_df %>% dplyr::select(date, USgas)
USgas_df_2$lag12 <- dplyr::lag(USgas_df_2$USgas, 12)
USgas_df_2 <- filter(USgas_df_2, !is.na(lag12))

Then we create the training and testing partitions.

train_df_2 <- USgas_df_2[1:(nrow(USgas_df_2) - 12),]
test_df_2 <- USgas_df_2[(nrow(USgas_df_2) - 12 + 1):nrow(USgas_df_2),]

Finally we convert the new dataframe into a time series (after removing the NA’s from the new lag12 column)

USgas_2 <- ts(USgas_df_2[,2], start = c(lubridate::year(min(USgas_df_2$date)), lubridate::month(min(USgas_df_2$date))), frequency = 12)
USgas_2_split <- ts_split(USgas_2, sample.out = 12)
train_2 <- USgas_2_split$train
test_2 <- USgas_2_split$test

Now we train the model. We see that all the variables are statistically significant.

tslm_2 <- tslm(train_2 ~ season + trend + lag12, data = train_df_2)
summary(tslm_2)

Call:
tslm(formula = train_2 ~ season + trend + lag12, data = train_df_2)

Residuals:
    Min      1Q  Median      3Q     Max 
-482.25  -69.02   -6.49   62.30  303.59 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1775.44299  175.14140  10.137  < 2e-16 ***
season2     -224.00793   43.75008  -5.120 7.24e-07 ***
season3     -344.73891   51.24064  -6.728 1.83e-10 ***
season4     -657.89645   73.72577  -8.924 3.14e-16 ***
season5     -792.70500   84.39483  -9.393  < 2e-16 ***
season6     -801.12959   85.67476  -9.351  < 2e-16 ***
season7     -674.81564   76.25415  -8.850 5.05e-16 ***
season8     -673.61978   75.05266  -8.975 2.25e-16 ***
season9     -821.10642   87.46764  -9.388  < 2e-16 ***
season10    -758.55725   82.17669  -9.231  < 2e-16 ***
season11    -585.17118   67.78203  -8.633 2.01e-15 ***
season12    -209.57316   43.91150  -4.773 3.54e-06 ***
trend          2.06445    0.20712   9.968  < 2e-16 ***
lag12          0.29126    0.06882   4.232 3.55e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 117.7 on 197 degrees of freedom
Multiple R-squared:  0.9287,    Adjusted R-squared:  0.924 
F-statistic: 197.3 on 13 and 197 DF,  p-value: < 2.2e-16

Analyzing the model

First we use the training set to forecast the testing values, and compare the fitted values with the real values.

fc_tslm2 <- forecast(tslm_2, h = 12, newdata = test_df_2)
accuracy(fc_tslm2, test_2)
##                        ME     RMSE      MAE        MPE     MAPE      MASE
## Training set 6.486123e-15 113.6858  86.1503 -0.2659394 4.188140 0.7632066
## Test set     1.523833e+02 185.0544 163.9276  5.9249393 6.453793 1.4522371
##                   ACF1 Theil's U
## Training set 0.5041734        NA
## Test set     0.0908631 0.6272176

We can see that there has been an improvement in the model’s behaviour, since the error metrics from the training and testing partitions are pretty close (this indicates that the model is probably not overfitting).

In this slide we visualize the fitted values by the model and the real values from the testing partition.

test_forecast(actual = USgas_2,
              forecast.obj = fc_tslm2,
              test = test_2)
checkresiduals(fc_tslm2)

## 
##  Ljung-Box test
## 
## data:  Residuals from Linear regression model
## Q* = 121.95, df = 10, p-value < 2.2e-16
## 
## Model df: 14.   Total lags used: 24

Conclusions

  • The model seems to be capturing better the patterns of the data, since most of the residuals aren’t correlated with their lags. But on the other hand, since there are some residuals correlated with their lags the model is not capturing all the patterns in the data.
  • Residuals seem to be fairly normally distributed.
  • All in all, this model seems to be performing better than the first one, but it isn’t perfect. A better approach is to use SARIMA models.

ARIMA and SARIMA Models

Lag Analysis : We can see there’s a linear decay in the acf plot, which indicates that the series is not stationary (this is caused bc of the effect of the trend). Also in the pacf we see that the series is very correlated with its first non-seasonal and seasonal lags.

par(mfrow = c(1,2))
acf(USgas, lag.max = 60)
pacf(USgas, lag.max = 60)

Lag Analysis (First order non-seasonal difference) : There still is a lot of linear decay and correlation with its lags in the pacf. Also, there still is a linear decay in the acf, indicating that there still is a trend (and therefore the series is not stationary).

USgas_diff_1 <- diff(USgas, 1)
par(mfrow = c(1,2))
acf(USgas_diff_1, lag.max = 60) 
pacf(USgas_diff_1, lag.max = 60)

Lag Analysis (First order non-seasonal difference): The first order difference stabilizes the series in the mean, but the variance is not very stable, so we will have to differenciate again.

autoplot(USgas_diff_1)+ ggtitle("US National Gas Consumption - First order non-seasonal difference") + theme(plot.title = element_text(hjust = 0.5))

Lag Analysis (First order non-seasonal difference and first order seasonal difference) : Since the series is very correlated with its seasonal lags, we differenciate with respect to the first seasonal lag (lag 12, since its a monthly series). We can see how the linear decay has been removed, and how the correlations tail off in both acf in pacf. This is an indicator that the series will follow an ARMA structure.

USgas_diff_1_12 <- diff(USgas_diff_1, 12)
par(mfrow = c(1,2))
acf(USgas_diff_1_12, lag.max = 60) 
pacf(USgas_diff_1_12, lag.max = 60)

Lag Analysis (First order non-seasonal difference and first order seasonal difference): We can see how the series has stabilized its mean and variance.

autoplot(USgas_diff_1_12)+ ggtitle("US National Gas Consumption - First order non-seasonal difference 
        and first order seasonal difference") + theme(plot.title = element_text(hjust = 0.5))

Tuning the parameters (I): As we saw previously the series will follow an ARMA structure. We know the parameters d=1 and D=1 (since we differenciated with respect to the first seasonal and non-seasonal lags). We create a table with the possible combinations of the parameters, and k (the result of adding the parameters) and later we will create a function that creates SARIMA models with the different combinations of parameters, and returns the best models (rated by their AIC).

p <- q <- P <- Q <- 0:2
d <- D <- 1
arima_grid <- expand.grid(p,d,q,P,D,Q)
names(arima_grid) <- c("p","d","q","P","D", "Q")
arima_grid$k <- rowSums(arima_grid)
arima_grid <- filter(arima_grid, k <= 6)
head(arima_grid, 3)
##   p d q P D Q k
## 1 0 1 0 0 1 0 2
## 2 1 1 0 0 1 0 3
## 3 2 1 0 0 1 0 4

Tuning the parameters (II): We create the arima_search2 function which trains in the training partition SARIMA models with different parameters combinations and returns us the model’s parameters and its respective AIC score (we filter those models with k <= 6). Then we select the 3 best models.

arima_search2 <- function(x){
        for(i in 1:nrow(x)){
                md <- NULL
                md <- arima(train, order = c(x$p[i], 1, x$q[i]), 
                            seasonal = list(order = c(x$P[i], 1, x$Q[i])))
                x$AIC[i] <- md$aic
                x <- x %>% arrange(AIC)
        }
        x
}
best_arima_models <- arima_search2(arima_grid)
best_arima_models <- best_arima_models[1:3,]
best_arima_models
##   p d q P D Q k      AIC
## 1 1 1 1 0 1 2 6 2568.698
## 2 1 1 1 0 1 1 5 2568.944
## 3 1 1 1 1 1 1 6 2569.577

The best models are (from best to worst) SARIMA(1,1,1)(0,1,2), SARIMA(1,1,1)(0,1,1), SARIMA(1,1,1)(1,1,1)

Analyzing SARIMA models performance

SARIMA(1,1,1)(0,1,2)

Training the model (I):

md_arima1 <- arima(train, order = c(1,1,1), seasonal = list(order = c(0,1,2)))
fc_arima1 <- forecast(md_arima1, h = 12)
accuracy(fc_arima1, test)
##                     ME      RMSE       MAE       MPE     MAPE      MASE
## Training set  6.599152  99.95595  74.19449 0.1511503 3.542092 0.6546500
## Test set     84.474454 128.91162 104.39337 3.1579162 4.037869 0.9211077
##                     ACF1 Theil's U
## Training set -0.00175609        NA
## Test set     -0.01579660 0.4434417

It has a similar MAPE in the training and testing partitions, so it doesn’t seem to be overfitting

Comparing fitted values and real values

test_forecast(actual = USgas,
              forecast.obj = fc_arima1,
              test = test)
checkresiduals(fc_arima1) 

## 
##  Ljung-Box test
## 
## data:  Residuals from ARIMA(1,1,1)(0,1,2)[12]
## Q* = 33.087, df = 20, p-value = 0.033
## 
## Model df: 4.   Total lags used: 24

Model conclusion

  • It seems to be capturing pretty well the patterns of the data
  • Residuals and their lags seem to be pretty independent. Residuals are fairly normally distributed and white noise

SARIMA(1,1,1)(0,1,1)

Training the model (I):

md_arima2 <- arima(train, order = c(1,1,1), seasonal = list(order = c(0,1,1)))
fc_arima2 <- forecast(md_arima2, h = 12)
accuracy(fc_arima2, test)
##                     ME     RMSE       MAE       MPE     MAPE      MASE
## Training set  6.170757 100.6778  74.15539 0.1331185 3.531331 0.6543049
## Test set     98.452650 135.5333 108.77917 3.6977179 4.170782 0.9598055
##                      ACF1 Theil's U
## Training set -0.001640912        NA
## Test set     -0.012855098     0.463

It has a similar MAPE in the training and testing partitions, so it doesn’t seem to be overfitting.

Comparing fitted values and real values

test_forecast(actual = USgas, 
              forecast.obj = fc_arima2,
              test = test)
checkresiduals(fc_arima2) 

## 
##  Ljung-Box test
## 
## data:  Residuals from ARIMA(1,1,1)(0,1,1)[12]
## Q* = 38.832, df = 21, p-value = 0.01028
## 
## Model df: 3.   Total lags used: 24

Model conclusion

  • Higher error rate than the first model (but not overfitting)
  • Residuals and their lags seem to be pretty independent. Residuals are fairly normally distributed and white noise.
  • Similar residuals compared to the first model.

SARIMA(1,1,1)(1,1,1)

Training the model (I):

md_arima3 <- arima(train, order = c(1,1,1), seasonal = list(order = c(1,1,1)))
fc_arima3 <- forecast(md_arima3, h = 12)
accuracy(fc_arima3, test)
##                     ME     RMSE       MAE       MPE     MAPE      MASE
## Training set  6.479363 100.2483  74.20165 0.1460463 3.538213 0.6547132
## Test set     91.201439 132.6028 107.00811 3.4167589 4.122941 0.9441786
##                      ACF1 Theil's U
## Training set -0.002037941        NA
## Test set     -0.017794105 0.4548421

It has a similar MAPE in the training and testing partitions, so it doesn’t seem to be overfitting.

Comparing fitted values and real values

test_forecast(actual = USgas, 
              forecast.obj = fc_arima3,
              test = test)
checkresiduals(fc_arima3) 

## 
##  Ljung-Box test
## 
## data:  Residuals from ARIMA(1,1,1)(1,1,1)[12]
## Q* = 35.567, df = 20, p-value = 0.01729
## 
## Model df: 4.   Total lags used: 24

Model conclusion

  • Higher error rate than the first two models (but not overfitting)
  • Residuals and their lags seem to be pretty independent. Residuals are fairly normally distributed and white noise.
  • Similar residuals compared to the first model.

auto.arima() model

Training the model (I):

md_arima_auto <- auto.arima(train) 
fc_arima_auto <- forecast(md_arima_auto, h = 12)
accuracy(fc_arima_auto, test)
##                      ME     RMSE      MAE        MPE     MAPE      MASE
## Training set  -4.776519 100.1783  74.7367 -0.4713371 3.592793 0.6594341
## Test set     124.548652 155.4699 131.1530  4.7905509 5.093099 1.1572192
##                     ACF1 Theil's U
## Training set -0.01466532        NA
## Test set     -0.14371060 0.5321727

It has a similar MAPE in the training and testing partitions, so it doesn’t seem to be overfitting.

Comparing fitted values and real values

test_forecast(actual = USgas, 
              forecast.obj = fc_arima_auto,
              test = test)
checkresiduals(fc_arima_auto) 

## 
##  Ljung-Box test
## 
## data:  Residuals from ARIMA(2,0,2)(1,1,1)[12] with drift
## Q* = 32.18, df = 17, p-value = 0.01429
## 
## Model df: 7.   Total lags used: 24

Model conclusion

  • Higher AIC than the first three models
  • Higher error rate than the first three models (but not overfitting)
  • Residuals and their lags are pretty independent. Residuals are normally distributed and white noise.
  • Better residuals than the other models.

Final decision

Finally after analyzing all the models, the best one is clearly the first one: ARIMA(1,1,1)(0,1,2), so this is the model we are going to use to forecast the data.

Forecast

Forecasting (I)

md_arima_final <- arima(USgas, order = c(1,1,1), seasonal = list(order = c(0,1,2)))
fc_arima_final <- forecast(md_arima_final, h = 12)

Forecasting next year (II)

autoplot(fc_arima_final) + 
        ggtitle("Forecast: US Gas Natural Gas Consumption using SARIMA(1,1,1)(0,1,2)") + 
        xlab("Year") + ylab("Billion Cubic Feet")

Forecasting next year - Zoomed (III)

autoplot(fc_arima_final) + 
        ggtitle("Forecast: US Gas Natural Gas Consumption using SARIMA(1,1,1)(0,1,2)") + 
        xlab("Year") + ylab("Billion Cubic Feet") + xlim(as.Date(c("2014-01-01", "2021-01-01")))

Forecasting next 3 years (IV)

fc_arima_final_2 <- forecast(md_arima_final, h = 36)
autoplot(fc_arima_final_2) + ggtitle("Forecast: US Gas Natural Gas Consumption using SARIMA(1,1,1)(0,1,2)") + 
        xlab("Year") + ylab("Billion Cubic Feet")

Forecasting next 3 years - Zoomed (V)

autoplot(fc_arima_final_2) + ggtitle("Forecast: US Gas Natural Gas Consumption using SARIMA(1,1,1)(0,1,2)") + 
        xlab("Year") + ylab("Billion Cubic Feet") + xlim(as.Date(c("2014-01-01", "2022-11-01")))

Thanks for your attention!