Parameter Glossary

This page explains the most common parameters you will see in manifest estimators blocks.

Temporal estimators (RS, DFA, DMA, AbsoluteMoment, Variance, VarianceResidual)

Parameter	Type	Default	Description
n_bootstrap	int	200	Number of bootstrap replicates for confidence intervals. Set to 0 to skip bootstrap CIs.
bootstrap_block_len	int	max(4, n//10)	Block length in samples for the circular block bootstrap.
ci_levels	list[float]	[0.95]	Nominal coverage levels for symmetric percentile intervals.
min_scale	int	varies	Minimum block size or scale (in samples) for RS, DFA, DMA, and aggregation estimators. RS defaults to 8.
max_scale	int	varies	Maximum block size or scale (in samples). RS defaults to n//2 so each fitted scale has at least two subseries.
detrend_order	int	1	Polynomial detrending order for DFA and VarianceResidual.
scale_ratio	float	1.5	Geometric spacing factor between consecutive aggregation scales.
use_anis_lloyd_correction	bool	False	(RS only) Divide each scale's average R/S value by the Anis-Lloyd white-noise expectation before fitting the slope.

Spectral estimators (GPH, Periodogram, WhittleMLE, ModifiedLocalWhittle)

Parameter	Type	Default	Description
n_bootstrap	int	200	Number of bootstrap replicates for CIs.
bootstrap_block_len	int	max(4, n//10)	Block length for the circular block bootstrap.
ci_levels	list[float]	[0.95]	Nominal coverage levels.
m	int	varies	Number of low-frequency Fourier frequencies used. GPH and Periodogram default to n^0.5; WhittleMLE defaults to n//8; ModifiedLocalWhittle defaults to n^0.55.
taper	str	"none"	Spectral taper. "none" uses the raw periodogram; "cosine" applies a cosine bell (Hann-type) window to reduce spectral leakage.

Geometric estimators (Higuchi, GHE)

Parameter	Type	Default	Description
k_max	int	max(8, min(64, n//8))	Maximum lag / block size for Higuchi curve-length calculation.
n_scales	int	16	Number of geometrically spaced lags for GHE.
h_min	int	1	Minimum lag for GHE.
flat_slope_tol	float	0.08	(GHE only) Threshold below which the log-log slope is treated as flat and the estimate is clamped to 0.5. Set to 0.0 to disable.

Wavelet estimators (WaveletOLS, WaveletAbryVeitch, WaveletBardet, WaveletJensen, WaveletWhittle)

Parameter	Type	Default	Description
n_bootstrap	int	200	Number of bootstrap replicates for CIs.
bootstrap_block_len	int	max(4, n//10)	Block length for the circular block bootstrap.
ci_levels	list[float]	[0.95]	Nominal coverage levels.
wavelet	str	"db4"	Wavelet family passed to pywt.wavedec.
max_level	int	varies	Maximum decomposition level. Defaults to the largest usable level minus boundary scales.

Data-driven estimators (MLRandomForest, MLSVR, MLCNN, MLLSTM)

Parameter	Type	Default	Description
model_path	str	None	Path to a pre-trained model artefact. If absent, the estimator trains from the manifest's ml_training block.
max_lag	int	16	(MLRandomForest, MLSVR) Number of autocorrelation lags included in the feature vector. Changing this from the value used during training will raise a max_lag_mismatch error instead of silently producing garbage.
n_estimators	int	100	(MLRandomForest) Number of trees in the random forest.
max_depth	int	None	(MLRandomForest) Maximum depth of each tree. None means fully grown.
min_samples_leaf	int	1	(MLRandomForest) Minimum samples per leaf.
C	float	10.0	(MLSVR) SVR regularization parameter.
epsilon	float	0.03	(MLSVR) SVR epsilon-tube margin.
kernel	str	"rbf"	(MLSVR) Kernel type.
sequence_length	int	256	(MLCNN, MLLSTM) Length every input series is resampled to before being fed to the network. Values smaller than 8 raise an error.
conv1_channels	int	16	(MLCNN) First convolution channel count.
conv2_channels	int	32	(MLCNN) Second convolution channel count.
hidden_size	int	32	(MLLSTM) LSTM hidden size per layer.
num_layers	int	1	(MLLSTM) Number of stacked LSTM layers. Values >1 trigger inter-layer dropout.
dropout	float	0.2	(MLCNN, MLLSTM) Dropout probability applied after conv/LSTM layers and in the MLP head. Set to 0.0 to disable (LSTM will still use 0.2 when num_layers > 1 to avoid PyTorch errors).
learning_rate	float	0.001	(MLCNN, MLLSTM) Adam learning rate.
weight_decay	float	1e-4	(MLCNN, MLLSTM) Adam weight-decay (L2 regularization).
batch_size	int	16	(MLCNN, MLLSTM) Training mini-batch size.
epochs	int	8	(MLCNN, MLLSTM) Number of training epochs.

Execution block

Parameter	Type	Default	Description
max_workers	int	1	Thread-pool size for parallel estimator fits.
estimate_cache_dir	str	None	Directory for on-disk estimate caches.
cache_read	bool	True	Allow reading from the estimate cache.
cache_write	bool	True	Allow writing to the estimate cache.