Configuration

Every stage of mobts is controlled by small configuration dataclasses with sensible defaults. This page lists what each one controls and how to override it.

Shared configuration

Used by both preprocessing and imputation.

ColumnsConfig

The canonical internal names mobts renames your columns to during processing. Input and output column names are always your own (see Data format). This only matters if you are working with the package’s internals directly.

Field	Default
counter	'counter'
timestamp	'timestamp'
count	'count'
weekday	'weekday'
week_num	'week_num'
hour	'hour'
how (hour of week)	'how'
date	'date'

SparsityConfig

Controls when a counter is dropped for having too few valid observations.

Field	Default
sparse_threshold	0.5

A counter is dropped if the fraction of missing counts exceeds this threshold. Setting it to 1.0 effectively disables sparse-counter dropping, since no counter’s missing rate can exceed 100%.

Preprocessing configuration

PreprocessConfig

Controls the always-on measurement-error rules described in Preprocessing.

Field	Default	Meaning
low_abs_daily	5	Absolute low-count threshold (daily)
low_rel_daily	0.01	Low-count threshold as a fraction of the counter’s median
low_run_min_daily	2	Minimum consecutive low-count days before they’re nulled
zero_rate_max	0.05	Max allowed rate of zero observations for an hour to be considered a real zero (hourly)
zero_run_min	6	Minimum consecutive zero hours before they’re nulled
island_max_len	6	Max length of a real-looking “island” surrounded by gaps to still be nulled
surround_min_len	12	Minimum surrounding gap length for the island rule above to apply

Fixed, not currently overridable: night_hours ([1, 2, 3, 4, 5, 6]). The hours excluded from the hourly zero-rate check.

STLConfig (preprocessing)

Parameters for the STL decomposition used to compute each observation’s outlier score. Only applies to daily data .Hourly outlier scoring uses a different, lighter-weight method.

Field	Default
period	28
robust	False

OutlierConfig

The threshold an outlier score has to cross to be flagged and replaced with NaN.

Field	Default
threshold_daily	20
threshold_hourly	45

PlotConfig

Cosmetic and sampling settings for plot_outliers().

Field	Default
ncols	3
figsize_width	15
min_fig_height	10
height_per_row	3
linewidth_d	0.5
linewidth_h	0.3
x_label_rotation	45
default_max_counters	30

default_max_counters is only used when plot_outliers() is called without an explicit counters list or max_counters value .It caps how many counters get randomly sampled for the plot in that case.

PipelineConfig

Aggregates all of the above into one object, passed to preprocess() as a whole:

from mobts import preprocess
from mobts.configs.config_preprocessing import PipelineConfig, PreprocessConfig, OutlierConfig

custom_cfg = PipelineConfig(
    preprocess=PreprocessConfig(low_abs_daily=10),
    outliers=OutlierConfig(threshold_daily=15),
)

pp = preprocess(cfg=custom_cfg)

You only need to override the sub-config(s) you actually want to change. Anything you do not set keeps its default.

Imputation configuration

Imputation doesn’t have a single aggregating config object. Each config is passed to impute() individually.

STLConfig (imputation)

A different class from the preprocessing STLConfig above, despite the same name .This one controls the STL-based gap-filling used as imputation’s univariate fallback.

Field	Default
rolling_median_window	2
rolling_median_min_valid	1

Fixed, not currently overridable: clip_lower (0), stl_robust (False), stl_season_daily (7), stl_season_hourly (168).

DonorsConfig

Thresholds governing donor eligibility and selection for the regression and scaled-median methods.

Field	Default	Meaning
max_donor_rate	0.5	Fraction of correlated counters considered as candidate donors
top_k_donor	21	Maximum number of donors used per target counter
min_mutual_days / min_mutual_hours	60 / 1440	Minimum overlapping history required between target and donors
min_pred_days / min_pred_hours	30 / 720	Minimum length of the missing period for coverage checks to apply
min_pred_coverage	1	Minimum donor coverage required over the missing period
sm_min_overlap_day / sm_min_overlap_hour	60 / 1440	Minimum overlap required specifically for scaled-median imputation

OutputConfig

Names of the output columns imputation produces, and the labels written into imputation_method.

Field	Default
col_reg_imputed	'multi-variate(regression)'
col_sm_imputed	'multi-variate(scaled-medians)'
col_stl_imputed	'uni-variate(STL)'
col_final	'count_imputed/clean'
col_method_used	'imputation_method'

Fixed, not currently overridable: col_intp ('count_intp'), an internal interpolation column not present in final output.

Note

Column names containing / or () (the current col_reg_imputed/col_sm_imputed/col_stl_imputed/col_final defaults) work fine with normal pandas indexing (df['multi-variate(regression)']), but will break df.query()/df.eval() unless backtick-escaped, and can’t be accessed as attributes (df.count_imputed/clean is not valid Python).

Putting it together

from mobts import impute
from mobts.configs.config_imputation import STLConfig, OutputConfig

imp = impute(
    stl_cfg=STLConfig(rolling_median_window=3),
    out_cfg=OutputConfig(col_final='final_count'),
)