tspy.data_structures.multi_time_series.SegmentMultiTimeSeries module

class tspy.data_structures.multi_time_series.SegmentMultiTimeSeries.SegmentMultiTimeSeries(tsc, j_mts)

Bases: tspy.data_structures.multi_time_series.MultiTimeSeries.MultiTimeSeries

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

Attributes

keys

Returns

Methods

aggregate(zero, seq_func, comb_func)	aggregate all series in this multi-time-series to produce a single value
aggregate_series(list_to_val_func)	aggregate all time-series in the multi-time-series using a summation function to produce a single time-series
aggregate_series_with_key(list_to_val_func)	aggregate all time-series with key in the multi-time-series using a summation function to produce a single time-series
align(key[, interp_func])	align all time-series on a key
cache([cache_size])	suggest to the multi-time-series to cache values
collect([inclusive])	collect and materialize this multi-time-series
collect_series(key[, inclusive])	get a collection of observations given a key
describe()	retrieve a NumStats object per time-series computed from all values in this multi-time-series (double)
fillna(interpolator[, null_value])	produce a new multi-time-series which is the result of filling all null values.
filter(func)	produce a new multi-time-series which is the result of filtering by each observation’s value given a filter function.
filter_series(func)	filter each time-series by its time-series object
filter_series_key(func)	filter each time-series by its key
flatten([key_func])	converts this segment-multi-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 for each time-series
full_align(multi_time_series[, …])	align two multi-time-series based on a temporal full join strategy and optionally interpolate missing values
full_join(multi_time_series[, join_func, …])	join two multi-time-series based on a temporal full join strategy and optionally interpolate missing values
get_time_series(key)	get a time-series given a key
get_values(start, end[, inclusive])	get all values between a range in this multi-time-series
inner_align(multi_time_series)	align two multi-time-series based on a temporal inner join strategy
inner_join(multi_time_series[, join_func])	join two multi-time-series based on a temporal inner join strategy
left_align(multi_time_series[, interp_func])	align two multi-time-series based on a temporal left join strategy and optionally interpolate missing values
left_join(multi_time_series[, join_func, …])	join two multi-time-series based on a temporal left join strategy and optionally interpolate missing values
left_outer_align(multi_time_series[, …])	align two multi-time-series based on a temporal left outer join strategy and optionally interpolate missing values
left_outer_join(multi_time_series[, …])	join two multi-time-series based on a temporal left outer join strategy and optionally interpolate missing values
map(func)	produce a new multi-time-series where each observation’s value in this multi-time-series is mapped to a new observation value
map_series(func)	map each ObservationCollection to a new collection of observations
map_series_key(func)	map each time-series key to a new key
map_series_with_key(func)	map each ObservationCollection to a new collection of observations giving access to each time-series key
pair_wise_transform(binary_transform)	produce a new multi-time-series which is the product of performing a pair-wise transform against all combination of keys
print([start, end, inclusive])	print this multi-time-series
reduce(func)	reduce each time-series in this multi-time-series to a single value
reduce_range(func, start, end[, inclusive])	reduce each time-series in this multi-time-series to a single value given a range
resample(period, interp_func)	produce a new multi-time-series by resampling each time-series to a given periodicity
right_align(multi_time_series[, interp_func])	align two multi-time-series based on a temporal right join strategy and optionally interpolate missing values
right_join(multi_time_series[, join_func, …])	join two multi-time-series based on a temporal right join strategy and optionally interpolate missing values
right_outer_align(multi_time_series[, …])	align two time-series based on a temporal right outer join strategy and optionally interpolate missing values
right_outer_join(multi_time_series[, …])	join two multi-time-series based on a temporal right outer join strategy and optionally interpolate missing values
segment(window[, step, enforce_bounds])	produce a new segment-multi-time-series from a performing a sliding-based segmentation over each time-series
segment_by(func)	produce a new segment-multi-time-series from a performing a group-by operation on each observation’s value for each time-series
segment_by_anchor(func, left_delta, right_delta)	produce a new segment-multi-time-series from performing an anchor-based segmentation over each time-series.
segment_by_changepoint([change_point])	produce a new segment-multi-time-series from performing a chang-point based segmentation.
segment_by_time(window, step)	produce a new segment-multi-time-series from a performing a time-based segmentation over each time-series
time_series(key)	get a time-series given a key
to_df([format, inclusive])	convert this multi-time-series to a pandas dataframe.
to_df_instants([inclusive])	convert this multi-time-series to an observations pandas dataframe.
to_df_observations([inclusive])	convert this multi-time-series to an observations pandas dataframe.
to_segments(segment_transform)	produce a new segment-multi-time-series from a segmentation transform
transform(*args)	produce a new multi-time-series which is the result of performing a transforming over each time-series.
trs(key)	get a time-series time-reference-system given a key
uncache()	remove the multi-time-series caching mechanism
with_trs([granularity, time_tick])	create a new multi-time-series with its timestamps mapped based on a granularity and start_time.
write([start, end, inclusive])	create a multi-time-series-writer given a range

flatten(key_func=None)

converts this segment-multi-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 multi-time-series

>>> import tspy
>>> mts_orig = tspy.multi_time_series.dict({'a': tspy.data_structures.list([1,2,3]), 'b': tspy.data_structures.list([4,5,6])})
>>> mts_orig
a time series
------------------------------
TimeStamp: 0     Value: 1
TimeStamp: 1     Value: 2
TimeStamp: 2     Value: 3
b time series
------------------------------
TimeStamp: 0     Value: 4
TimeStamp: 1     Value: 5
TimeStamp: 2     Value: 6

segment the multi-time-series using a simple sliding window

>>> mts_sliding = mts_orig.segment(2)
>>> mts_sliding
a time series
------------------------------
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)]
b time series
------------------------------
TimeStamp: 0     Value: original bounds: (0,1) actual bounds: (0,1) observations: [(0,4),(1,5)]
TimeStamp: 1     Value: original bounds: (1,2) actual bounds: (1,2) observations: [(1,5),(2,6)]

flatten the segments into a single multi-time-series

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