# data[].attributes.body
From early 1994 to late 2010 sea ice numbers lowered by 0.0625 million square km per year.

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From 1990 to 2011 arctic sea ice levels declined by  0.049 square km per year
9.45 -8.37 = 1.08
Gradient = 1.08/22 = 0.04909090909

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From 1995 to mid 2007, the arctic sea ice declined by 0.125 million square km

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From 1996 to late 2007 sea ice numbers lowered by 0.125 million square kilometres per year 

# data[].attributes.subject
From 1996 to late 2007 sea ice numbers lowered by 0.125 million square kilometres per year 

# data[].attributes.body
From 1996 to late 2007, sea ice numbers decline by 0.09 millions of square kilometres in the Arctic, per year

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9.5 - 8.75 = 0.75
Gradient = 0.75⧸16.25 = 0.046 arctic sea - ice
From early 1994 to mid 2010 the number of arctic sea - ice declined by 0.046 arctic sea - ice per year.

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From 1995 to 2011 arctic ice per million square km declined at 0.0625 per year

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From early 1998 to late 2005 arctic numbers remain fairly consistent with a slight decrease of 0.657 per year

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From early 1998 to late 2005 arctic numbers remain fairly consistent with a slight decrease of 0.657 per year

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Gradient : 4.6 / 7 = 0.657

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From 1997 to late 2007 sea arctic levels slowly decreased by 0.13 million square km of ice per year.

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Quarterly - a set of observations, results, or other data obtained over a period of time, usually at regular intervals: Monthly sales figures, quarterly inventory data, and daily bank balances are all time series.

Highly seasonal - Seasonality refers to periodic fluctuations in certain business areas that occur regularly based on a particular season.

Seasonally adjusted - Seasonal adjustment is a statistical method for removing the seasonal component of a time series that exhibits a seasonal pattern. It is usually done when wanting to analyse the trend, and cyclical deviations from trend, of a time series independently of the seasonal components.

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Quarterly

The quarter is how it is measured

Seasonal

Any predictable change or pattern in a time series that recurs or repeats over a one-year period can be said to be seasonal

Seasonally adjusted

Seasonal adjustment is a statistical method for removing the seasonal component of a time series that exhibits a seasonal pattern.

Seasonal average

The average for the season

Moving average

In time series analysis, the moving-average (MA) model is a common approach for modeling univariate time series. The moving-average model specifies that the output variable depends linearly on the current and various past values of a stochastic (imperfectly predictable) term

Irregular

The irregular component of a time series is the residual time series after the trend-cycle and the seasonal components (including calendar effects) have been removed. It corresponds to the high frequency fluctuations of the series.

Seasonal component

The seasonal component is that part of the variations in a time series representing intra-year fluctuations that are more or less stable year after year with respect to timing, direction and magnitude. Context: The seasonal component is also referred to as the seasonality of a time series.

# data[].attributes.body
is a statistical method for removing the seasonal component of a time series that exhibits a seasonal pattern. It is usually done when wanting to analyse the trend, and cyclical deviations from trend, of a time series independently of the seasonal components.

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Quarterly- The period that the variable is taken.

Seasonality- Presence of variation that occur at specific regular intervals less than a year e.g quarterly, weekly.

Adjusted- Is a statistical method for removing the seasonal component of a time series that exhibits a seasonal pattern.

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Seasonal- A seasonal pattern exists when a series is influenced by seasonal factors 

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Seasonal- A seasonal pattern exists when a series is influenced by seasonal factors 

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Seasonal Component: The seasonal component is a sum of the periodical short-term movements having a length of less than one year. It represents intra-year fluctuations that repeat every year with respect to timing, direction and magnitude.

Seasonal adjustment: is a sophisticated analysis technique that is aiming for estimating and removing seasonal and calendar related movements from the time series.

moving average: a succession of averages derived from successive segments (typically of constant size and overlapping) of a series of values.

Seasonal: fluctuating or restricted according to the season or time of year.

irregular: not even or balanced in shape or arrangement.

# data[].attributes.headline
Seasonally adjusted-It is usually done when wanting to analyse the trend, and cyclical deviations from trend, of a time series independently of the seasonal components. 

# data[].attributes.subject
Seasonally adjusted-It is usually done when wanting to analyse the trend, and cyclical deviations from trend, of a time series independently of the seasonal components. 

# data[].attributes.body
Seasonal adjustment is a
analysis technique that is aiming for estimating and removing seasonal and calendar related movements from the time series.

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Seasonal
The presence of variations that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly.

Quarterly
A trend that is occurring once every quarter of a year

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Time series Economic
Quarterly - Quarterly data would mean that you have one data point per quarter.
We want to be seeing - particular time in the series
Seasonal -A seasonal pattern exists when a series is influenced by seasonal factors (e.g., the quarter of the year, the month, or day of the week)
Seasonally adjusted - It is normal to report seasonally adjusted data for unemployment rates to reveal the underlying trends and cycles in labor markets.
Seasonal average -
Moving average - The moving-average model specifies that the output variable depends linearly on the current and various past values of a stochastic (imperfectly predictable) term.
Irregular - Unevenly spaced time series naturally occur in many industrial and scientific domains: natural disasters such as earthquakes, floods, or volcanic eruptions typically occur at irregular time intervals.
Seasonal component - A seasonal pattern exists when a series is influenced by seasonal factors (e.g., the quarter of the year, the month, or day of the week). Seasonality is always of a fixed and known period. A cyclic pattern exists when data exhibit rises and falls that are not of fixed period.

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Seasonality is a characteristic of a time series in which the data experiences regular and predictable changes that recur every calendar year. Any predictable change or pattern in a time series that recurs or repeats over a one-year period can be said to be seasonal.

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Seasonality is a characteristic of a time series in which the data experiences regular and predictable changes that recur every calendar year. Any predictable change or pattern in a time series that recurs or repeats over a one-year period can be said to be seasonal.

# data[].attributes.body
Definition: The seasonal component is that part of the variations in a time series representing intra-year fluctuations that are more or less stable year after year with respect to timing, direction and magnitude. Context: The seasonal component is also referred to as the seasonality of a time series.

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In time series data, seasonality is the presence of variations that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly.

# data[].attributes.subject
In time series data, seasonality is the presence of variations that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly.

# data[].attributes.body
Moving average - In time series analysis, the moving-average (MA) model is a common approach for modeling univariate time series. The moving-average model specifies that the output variable depends linearly on the current and various past values of a stochastic (imperfectly predictable) term.

# data[].attributes.body
Quarterly means a trend that is occurring once every quarter of a year in a time series

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Irregular - The other variations in time-series data that are not identified as part of the trend component, cyclical component or seasonal component. They mostly consist of variations that don’t have a clear pattern.

# data[].attributes.subject
Irregular - The other variations in time-series data that are not identified as part of the trend component, cyclical component or seasonal component. They mostly consist of variations that don’t have a clear pattern.