tspy.data_structures.time_series.SegmentTimeSeries module

class tspy.data_structures.time_series.SegmentTimeSeries.SegmentTimeSeries(tsc, j_ts, trs=None)

Bases: tspy.data_structures.time_series.TimeSeries.TimeSeries

A special form of time-series that consists of observations with a value of type Segment

Attributes

trs

Returns

Methods

cache([cache_size])	suggest to the time-series to cache values
collect([inclusive])	collect all observations in this time-series
concat(other_time_series)	produce a new time-series which is the result of concatenating two time-series
count([inclusive])	count the current number of observations in this time-series
describe()	retrieve time-series statistics computed from all values in this time-series
fillna(interpolator[, null_value])	produce a new time-series which is the result of filling all null values.
filter(func)	produce a new time-series which is the result of filtering by each observation’s value given a filter function.
flatmap(func)	produce a new time-series where each observation’s value in this time-series is mapped to 0 to N new values.
flatten([key_func])	converts this segment-time-series into a multi-time-series where each time-series will be the result of a single segment
forecast(num_predictions, fm[, …])	forecast the next num_predictions using a forecasting model
full_align(time_series[, left_interp_func, …])	align two time-series based on a temporal full join strategy and optionally interpolate missing values
full_join(time_series[, join_func, …])	join two time-series based on a temporal full join strategy and optionally interpolate missing values
get_values(start, end[, inclusive])	get all values between a range in this time-series
inner_align(time_series)	align two time-series based on a temporal inner join strategy
inner_join(time_series[, join_func])	join two time-series based on a temporal inner join strategy
lag(lag_amount)	produce a new time-series which is a lagged version of the current time-series.
left_align(time_series[, interp_func])	align two time-series based on a temporal left join strategy and optionally interpolate missing values
left_join(time_series[, join_func, interp_func])	join two time-series based on a temporal left join strategy and optionally interpolate missing values
left_outer_align(time_series[, interp_func])	align two time-series based on a temporal left outer join strategy and optionally interpolate missing values
left_outer_join(time_series[, join_func, …])	join two time-series based on a temporal left outer join strategy and optionally interpolate missing values
map(func)	produce a new time-series where each observation’s value in this time-series is mapped to a new observation value
map_with_index(func)	produce a new time-series where each observation’s value in this time-series is mapped given the old value and an index to a new observation value
print([start, end, inclusive, human_readable])	print this time-series
reduce(*args)	reduce this time-series or two time-series to a single value
resample(period, func)	produce a new time-series by resampling the current time-series to a given periodicity
right_align(time_series[, interp_func])	align two time-series based on a temporal right join strategy and optionally interpolate missing values
right_join(time_series[, join_func, interp_func])	join two time-series based on a temporal right join strategy and optionally interpolate missing values
right_outer_align(time_series[, interp_func])	align two time-series based on a temporal right outer join strategy and optionally interpolate missing values
right_outer_join(time_series[, join_func, …])	join two time-series based on a temporal right outer join strategy and optionally interpolate missing values
segment(window[, step, enforce_size])	produce a new segment-time-series from a performing a sliding-based segmentation over the time-series
segment_by(func)	produce a new segment-time-series from a performing a group-by operation on each observation’s value
segment_by_anchor(func, left_delta, right_delta)	produce a new segment-time-series from performing an anchor-based segmentation over the time-series.
segment_by_changepoint([change_point])	produce a new segment-time-series from performing a chang-point based segmentation.
segment_by_marker(*args, **kwargs)	produce a new segment-time-series from performing a marker based segmentation.
segment_by_time(window, step)	produce a new segment-time-series from a performing a time-based segmentation over the time-series
shift(shift_amount[, default_value])	produce a new time-series which is a shifted version of the current time-series.
to_df([inclusive])	convert this time-series to a pandas dataframe
to_segments(segment_transform)	produce a new segment-time-series from a segmentation transform
transform(*args)	produce a new time-series which is the result of performing a transforming over the time-series.
uncache()	remove the time-series caching mechanism
with_trs([granularity, start_time])	create a new time-series with its timestamps mapped based on a granularity and start_time.
write([start, end, inclusive])	create a time-series-writer given a range

cache(cache_size=None)

suggest to the time-series to cache values

Parameters

cache_sizeint, optional

the max cache size (default is max long)

Returns

TimeSeries

a new time-series

Notes

this is a lazy operation and will only suggest to the time-series to save values once computed

flatmap(func)

produce a new time-series where each observation’s value in this time-series is mapped to 0 to N new values.

Parameters

funcfunc

value mapping function which returns a list of values

Returns

TimeSeries

a new time-series with its values flat-mapped

Notes

an observations time-tick will be duplicated if a single value maps to multiple values

Examples

create a simple time-series

>>> import tspy
>>> ts_orig = tspy.time_series([1, 2, 3])
>>> ts_orig
TimeStamp: 0     Value: 1
TimeStamp: 1     Value: 2
TimeStamp: 2     Value: 3

flat map each time-series observation value by duplicating the value

>>> ts = ts_orig.flatmap(lambda x: [x, x])
>>> ts
TimeStamp: 0     Value: 1
TimeStamp: 0     Value: 1
TimeStamp: 1     Value: 2
TimeStamp: 1     Value: 2
TimeStamp: 2     Value: 3
TimeStamp: 2     Value: 3

flatten(key_func=None)

converts this segment-time-series into a multi-time-series where each time-series will be the result of a single segment

Parameters

key_funcfunc, optional

operation where given a segment, produce a unique key (default is create key based on start of segment)

Returns

MultiTimeSeries

a new multi-time-series

Notes

this is not a lazy operation and will materialize the time-series

Examples

create a simple time-series

>>> import tspy
>>> ts_orig = tspy.data_structures.list([1,2,3,4,5,6])
>>> ts_orig
TimeStamp: 0     Value: 1
TimeStamp: 1     Value: 2
TimeStamp: 2     Value: 3
TimeStamp: 3     Value: 4
TimeStamp: 4     Value: 5
TimeStamp: 5     Value: 6

segment the time-series using a simple sliding window

>>> ts_sliding = ts_orig.segment(2)
>>> ts_sliding
TimeStamp: 0     Value: original bounds: (0,1) actual bounds: (0,1) observations: [(0,1),(1,2)]
TimeStamp: 1     Value: original bounds: (1,2) actual bounds: (1,2) observations: [(1,2),(2,3)]
TimeStamp: 2     Value: original bounds: (2,3) actual bounds: (2,3) observations: [(2,3),(3,4)]
TimeStamp: 3     Value: original bounds: (3,4) actual bounds: (3,4) observations: [(3,4),(4,5)]
TimeStamp: 4     Value: original bounds: (4,5) actual bounds: (4,5) observations: [(4,5),(5,6)]

flatten the segments into a single multi-time-series

>>> mts = ts_sliding.flatten()
>>> mts
0 time series
------------------------------
TimeStamp: 0     Value: 1
TimeStamp: 1     Value: 2
1 time series
------------------------------
TimeStamp: 1     Value: 2
TimeStamp: 2     Value: 3
2 time series
------------------------------
TimeStamp: 2     Value: 3
TimeStamp: 3     Value: 4
3 time series
------------------------------
TimeStamp: 3     Value: 4
TimeStamp: 4     Value: 5
4 time series
------------------------------
TimeStamp: 4     Value: 5
TimeStamp: 5     Value: 6

map(func)

produce a new time-series where each observation’s value in this time-series is mapped to a new observation value

Parameters

funcfunc

value mapping function

Returns

TimeSeries

a new time-series with its values re-mapped

Examples

create a simple time-series

>>> import tspy
>>> ts_orig = tspy.time_series([1, 2, 3])
>>> ts_orig
TimeStamp: 0     Value: 1
TimeStamp: 1     Value: 2
TimeStamp: 2     Value: 3

add one to each value of the time-series and produce a new time-series

>>> ts = ts_orig.map(lambda x: x + 1)
>>> ts
TimeStamp: 0     Value: 2
TimeStamp: 1     Value: 3
TimeStamp: 2     Value: 4

map each value of the time-series to a string and produce a new time-series

>>> ts = ts_orig.map(lambda x: "value - " + str(x))
>>> ts
TimeStamp: 0     Value: value - 1
TimeStamp: 1     Value: value - 2
TimeStamp: 2     Value: value - 3

map each value of the time-series to a string using high performant expressions

>>> from tspy.functions import expressions as exp
>>> ts = ts_orig.map(exp.add(exp.id(), 1))
TimeStamp: 0     Value: 2.0
TimeStamp: 1     Value: 3.0
TimeStamp: 2     Value: 4.0