Python : Approaches to mine for time ordered sequences
Tru to make some features from the datetime columns -
attr = ['Year', 'Month', 'Week', 'Day', 'Dayofweek', 'Is_month_end', 'Is_month_start',
'Is_quarter_end', 'Is_quarter_start', 'Is_year_end', 'Is_year_start']


you can google search about making date features.
2) Find the cumulative sum of the events -
cumsum_df_all = df.groupby('column')[other-columns].cumsum()
cumsum_14_day = (df.groupby('column')[other-columns].rolling(14, min_periods=1).sum().reset_index(0, drop=True)

like wise for 7,3,1 day, just look how frequent the data is and fast it changes.

Likewise find the roling std dev. and avg.

rolling_avg_14_day = (df.groupby('column')[other-columns].rolling(14,min_periods=1).mean().reset_index(0, drop=True))
set the rolling period accordingly.

find the difference between the event values for a specific period -
diff_7_day = df.groupby('column')[other-columns].diff(periods=7)


basically, we are trying to convert time ordered sequences to linear regression problem.