Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

Chapter 6
Time Series
temp_forecasted_data<-sum(data.frame(tempforecast$mean)*s + m)
weekjoyforecastvalues[nrow(weekjoyforecastvalues) + 1, ] <-
c( asmname, temp_forecasted_data)
}
weekjoyforecastvalues$value<-as.integer(weekjoyforecastvalues$value)
#weekjoyforecastvalues
(sum(weekjoyforecastvalues$value)- 53782605)/53782605
#103000000)/103000000
Missing Data
One important aspect of time series and many other data analysis work is
figuring out how to deal with missing data. In the previous code, you fill in
the missing record with the average value. This is fine when the number of
missing data instances is not very high. But if it is high, then the average of
the highest and lowest values is a better alternative.
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