2. Time-series analysis comprises the process
and mathematical set of tools used for
looking into time-series data to learn not
only what happened but also when and why it
happened, as well as what is most likely to
happen in the future.
The “time” element in time-series data means
that the data is ordered by time. In this type
of data, each entry is preceded and followed
by another and has a timestamp that
determines the order of the data.
3. Features: Time series analysis can be used to
track features like trend, seasonality, and
variability.
Forecasting: Time series analysis can aid in
the prediction of stock prices. It is used if you
would like to know if the price will rise or fall
and how much it will rise or fall.
Inferences: You can predict the value and
draw inferences from data using Time series
analysis.
5. A trend in time series data refers to a long-term upward or
downward movement in the data.
Upward Trend: A trend that shows a general increase over
time, where the values of the data tend to rise over time.
Downward Trend: A trend that shows a general decrease
over time, where the values of the data tend to decrease
over time.
Horizontal Trend: A trend that shows no significant change
over time, where the values of the data remain constant
over time.
Non-linear Trend: A trend that shows a more complex
pattern of change over time, including upward or
downward trends that change direction or magnitude over
time.
Damped Trend: A trend that shows a gradual decline in the
magnitude of change over time, where the rate of change
slows down over time.
6.
7. Seasonality in time series data refers to patterns
that repeat over a regular time period, such as a
day, a week, a month, or a year.
Weekly Seasonality: A type of seasonality that
repeats over a 7-day period.
Monthly Seasonality: A type of seasonality that
repeats over a 30- or 31-day period.
Annual Seasonality: A type of seasonality that
repeats over a 365- or 366-day period.
Holiday Seasonality: A type of seasonality that is
caused by special events such as holidays,
festivals, or sporting events.
8. Cyclicity in time series data refers to the
repeated patterns or periodic fluctuations
that occur in the data over a specific time
interval.
9. Irregularities in time series data refer to
unexpected or unusual fluctuations in the
data that do not follow the general pattern of
the data. These fluctuations can occur for
various reasons, such as measurement errors,
unexpected events, or other sources of
noise.
10.
11. Autocorrelation in time series data refers to
the degree of similarity between observations
in a time series as a function of the time lag
between them. Autocorrelation is a measure
of the correlation between a time series and a
lagged version of itself.
12. Noise in time series data refers to random
fluctuations or variations that are not due to
an underlying pattern or trend. It is typically
considered as any unpredictable and random
variation in the data. These fluctuations can
arise from various sources such as
measurement errors, random fluctuations in
the underlying process, or errors in data
recording or processing.
13. Classification: It identifies and assigns categories
to the data.
Curve Fitting: It plots data on a curve to
investigate the relationships between variables in
the data.
Descriptive Analysis: Patterns in time-series data,
such as trends, cycles, and seasonal variation,
are identified.
Explanative analysis: It attempts to comprehend
the data and the relationships between it and
cause and effect.
Segmentation: It splits the data into segments to
reveal the source data's underlying properties.
