B) Using an alpha of 0.35 and h = 100, use the ses() function from the...

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B) Using an alpha of 0.35 and h = 100, use the ses() function from the 'forecast' package to forecast the daily active energy and plot the model. What do you observe in the graph? (6 points) You will need to clean the data prior to fitting your models. To do so please do the following to a dataset containing daily active evergy: 1. Make all dates the same format. Hint - use this format: parse_time_data(data_columns, c(datetype1, datetype2)) 2. Remove any rows with NA values 3. Convert to xts 4. Fit ses() model Please the entire dataset to create the model. In [26] # SOLUTION BEGINS HERE # SOLUTION OPTION 1 library(xts) library (forecast) library (lubridate) data3 < datal %>% group_by(Date) %>% summarise (total_power sum(power_per_minute)) data3$Date B) Using an alpha of 0.35 and h = 100, use the ses() function from the 'forecast' package to forecast the daily active energy and plot the model. What do you observe in the graph? (6 points) You will need to clean the data prior to fitting your models. To do so please do the following to a dataset containing daily active evergy: 1. Make all dates the same format. Hint - use this format: parse_time_data(data_columns, c(datetype1, datetype2)) 2. Remove any rows with NA values 3. Convert to xts 4. Fit ses() model Please the entire dataset to create the model. In [26] # SOLUTION BEGINS HERE # SOLUTION OPTION 1 library(xts) library (forecast) library (lubridate) data3 < datal %>% group_by(Date) %>% summarise (total_power sum(power_per_minute)) data3$Date