The concept that time series analysis is meant to capture include the following features that
the time series tries to capture:
Trends-
these are consistent directional movement in a time series. They are either deterministic or
stochastic. The deterministic part provides an underlying rationale for the trend while the
stochastic aspect brings the random feature of the series that is unlikely to explain.
Seasonal Variation-
Most time series have a component of seasonality. This can be is series representing business
sales or climate level. In quantitative finance, the effects of seasonality is seen in
commodities.
Serial Dependence/ Correlation-
This occurs when time-series observations that close together in time tend to be correlated.
Factors such as volatility clustering are one of the aspects of serial correlation, it is one of
the important factors in finance. Quantitative modellers try to identify the structure of these
correlations because they allow them to improve the forecasts and thus improve the possibility
of the strategy-making profits.