Explanation:
A historigram is a graphical depiction of a time series that shows the changes that took place through time. An assessment of the set of prior data is a first stage in the prediction (or forecast) of a time series. In this scenario, a historigram could be beneficial.
Explanation:
All of the above are the movements within the secular trend.
Explanation:
The cyclical component of a time series refers to (regular or periodic) oscillations around the trend that exclude the irregular component, displaying a series of expansion and contraction phases.
Correct answer:
Smooth out the time series
Explanation:
Seasonal variation is possible in time series data. Seasonal variation, often known as seasonality, is a term that refers to cycles that occur on a regular basis. Seasonal variation is a term that refers to a pattern that repeats throughout the year, though it can also refer to patterns that repeat across any specified period.
Explanation:
The study of datasets that vary over time is referred to as time series data analysis. Time series datasets keep track of the same variable's observations at different times in time.
Explanation:
The residual time series after the trend-cycle and seasonal components (including calendar influences) have been removed is the irregular component of a time series. It corresponds to the series' high frequency variations.
Explanation:
In regression studies, one of the models utilized is the quadratic trend. It's worth noting that the linear regression model can't be utilized to explain complex models.
Explanation:
Deseasonalizing a time series entails estimating the S contribution and removing it by dividing Y by S (that is, computing Y/S). By computing Y/(TS) to give I, you can extend these principles even further to isolate the irregular variation.
