Total energy (GWh) 11000 10500 10000 9500 9000 8500 8000 Australia Total energy from quarter 1 2014 23 wwwwww. M SHISEN 34 234 234 234 234 234
The data for the energy production was recorded over the period from January 2014 to December 2022. This data has been averaged to quarterly data and the time series plot is give below in the Exhibit 14.
a) Discuss the time series components evident in this data series shown in Exhibit 14.
b) We carried out an estimated model of the household quarterly electricity demand from Qtr1 2014 to Qtr4 2022 as a
The variables are defined as:
Y variable:
- total_energy: = is the total energy production of different sources (GWh)
X variables:
- Time: = number of quarters since Qtr1 2014 to Qtr4 2022.
- Qtr 1: = 1 if the quarter is from January to March and 0 otherwise.
- Qtr 2: = 1 if the quarter is from April to June and 0 otherwise.
- Qtr 3: = 1 if the quarter is from July to September and 0 otherwise.
- Qtr 4: = 1 if the quarter is from September to December and 0 otherwise.
Quarter 4 was used as the base quarter.
The b) question is not specified please repost the option b) complete question.. Please find the complete solution of question a)
Time series analysis is a statistical method used to analyze and interpret data points
Time series analysis is a statistical method for analyzing and interpreting the data points that have been acquired over an ongoing interval of time. It is an essential instrument in a range of fields, such as economics, finance, engineering, climate science and so on.
Important concepts and components within time series analysis:
Time Series Data:
The series data comprises observations or measurements made in sequential fashion over a period of time. All the data points are linked to a special time index, making it possible to look at what changes happen in those variables over time.
Time Series Components:
- Time series data can typically be decomposed into several components, including:
- Trend: The long-term movement or pattern in the data, indicating whether it is increasing, decreasing, or stable.
- Seasonality: Periodic, reoccurring patterns in the data according to a particular period of time such as daily, monthly or quarterly cycles
- Cyclical: Long-term fluctuations, which are less frequent than seasonal and seasonality, tend to be associated with economic or business cycles.
- Irregular (or Residual): A random and unpredictable change in data that cannot be explained by trends, seasonal variations or cyclical fluctuations.
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