otp.agg.linear_regression#

linear_regression(running=False, all_fields=False, bucket_interval=0, bucket_time='end', bucket_units=None, bucket_end_condition=None, end_condition_per_group=False, boundary_tick_bucket='new')#

LINEAR_REGRESSION aggregation.

For each bucket, computes the linear regression parameters slope and intercept of specified input fields dependent_variable_field_name and independent_variable_field_name. Adds computed parameters as SLOPE and INTERCEPT fields in output time series. The relationship between the dependent variable (Y) and the independent variable (X) is defined by the formula: Y = SLOPE * X + INTERCEPT, where SLOPE and INTERCEPT are the calculated output parameters.

Parameters

running (bool, default=False) –
See Aggregation buckets guide to see examples of how this parameter works.

Specifies if the aggregation will be calculated as a sliding window. running and bucket_interval parameters determines when new buckets are created.
- running = True
  
  aggregation will be calculated in a sliding window.
  - bucket_interval = N (N > 0)
    
    Window size will be N. Output tick will be generated when tick “enter” window (arrival event) and when “exit” window (exit event)
  - bucket_interval = 0
    
    Left boundary of window will be set to query start time. For each tick aggregation will be calculated in the interval [start_time; tick_t] from query start time to the tick’s timestamp (inclusive).
- running = False (default)
  
  buckets partition the [query start time, query end time) interval into non-overlapping intervals of size bucket_interval (with the last interval possibly of a smaller size). If bucket_interval is set to 0 a single bucket for the entire interval is created.
  
  Note that in non-running mode OneTick unconditionally divides the whole time interval into specified number of buckets. It means that you will always get this specified number of ticks in the result, even if you have less ticks in the input data.
Default: False
all_fields (Union[bool, str], default=False) –
See Aggregation buckets guide to see examples of how this parameter works.
- all_fields = True
  
  output ticks include all fields from the input ticks
  - running = True
  an output tick is created only when a tick enters the sliding window
  - running = False
  fields of first tick in bucket will be used
- all_fields = False and running = True
  
  output ticks are created when a tick enters or leaves the sliding window.
- all_fields = “when_ticks_exit_window” and running = True
  
  output ticks are generated only for exit events, but all attributes from the exiting tick are copied over to the output tick and the aggregation is added as another attribute.
bucket_interval (int or float or Operation or OnetickParameter or symbol parameter or datetime offset object, default=0) –
Determines the length of each bucket (units depends on bucket_units).

If Operation of bool type is passed, acts as bucket_end_condition.

Bucket interval can also be set as a float value if bucket_units is set to seconds. Note that values less than 0.001 (1 millisecond) are not supported.

Bucket interval can be set via some of the datetime offset objects: otp.Milli, otp.Second, otp.Minute, otp.Hour, otp.Day, otp.Month. In this case you could omit setting bucket_units parameter.

Bucket interval can also be set with integer OnetickParameter or symbol parameter.
bucket_time (Literal['start', 'end'], default=end) –
Control output timestamp.
- start
  
  the timestamp assigned to the bucket is the start time of the bucket.
- end
  
  the timestamp assigned to the bucket is the end time of the bucket.
bucket_units (Optional[Literal['seconds', 'ticks', 'days', 'months', 'flexible']], default=None) –
Set bucket interval units.

By default, if bucket_units and bucket_end_condition not specified, set to seconds. If bucket_end_condition specified, then bucket_units set to flexible.

If set to flexible then bucket_end_condition must be set.

Note that seconds bucket unit doesn’t take into account daylight-saving time of the timezone, so you may not get expected results when using, for example, 24 * 60 * 60 seconds as bucket interval. In such case use days bucket unit instead. See example in onetick.py.agg.sum().
bucket_end_condition (condition, default=None) –
An expression that is evaluated on every tick. If it evaluates to “True”, then a new bucket is created. This parameter is only used if bucket_units is set to “flexible”.

Also can be set via bucket_interval parameter by passing Operation object.
end_condition_per_group (bool, default=False) –
Controls application of bucket_end_condition in groups.
- end_condition_per_group = True
  
  bucket_end_condition is applied only to the group defined by group_by
- end_condition_per_group = False
  
  bucket_end_condition applied across all groups
This parameter is only used if bucket_units is set to “flexible”.

When set to True, applies to all bucketing conditions. Useful, for example, if you need to specify group_by, and you want to group items first, and create buckets after that.
boundary_tick_bucket (Literal['new', 'previous'], default=new) –
Controls boundary tick ownership.
- previous
  
  A tick on which bucket_end_condition evaluates to “true” belongs to the bucket being closed.
- new
  
  tick belongs to the new bucket.
This parameter is only used if bucket_units is set to “flexible”

Examples

>>> data = otp.Ticks({'X': [10.0, 9.5, 8.0, 8.5], 'Y': [3.0, 5.0, 4.5, 3.5]})
>>> data = data.linear_regression(
...     dependent_variable_field_name=data['Y'],
...     independent_variable_field_name=data['X'],
... )  
>>> otp.run(data)  
        Time  SLOPE  INTERCEPT
0 2003-12-04   -0.3        6.7