Final answer:
For time series forecasting in a data visualization application, several modifications are performed, such as adding seasonality indicators, changing column data types to reflect categorical data, aggregating data,
Step-by-step explanation:
To modify the data from a Data Visualization Application for time series forecasting, one needs to perform specific operations. These include:
- Adding indicator columns for seasonality to account for regular patterns that repeat over a known period.
- Changing the data type of the Month column from numeric to categorical if the month information is provided. This reflects the non-numeric nature of months as categories rather than quantitative measurements.
- Adding a column to aggregate the number of COVID deaths by month. This is necessary for analyzing the data on a monthly basis and identifying trends over time.
- Removing columns that are irrelevant to the analysis to streamline the dataset and focus on the significant variables.
- Adding a column to account for the time period in the series, ensuring the chronological order of data is maintained and can be interpreted correctly.
A time series graph would be an important tool in displaying these modifications visually, as it allows trends and patterns over time to be easily spotted. Additionally, understanding the relationship between independent variables, like time (years), and dependent variables, such as the number of flu cases diagnosed, would help in interpreting the data plots and choosing the appropriate type for the visual representation of trends.