GitHub - views-platform/views-r2darts2: A time series deep learning suite designed for conflict forecasting within the VIEWS (Violence and Impacts Early-Warning System) ecosystem.

A time series forecasting package for conflict prediction within the VIEWS (Violence and Impacts Early-Warning System) ecosystem. Built on Darts and PyTorch Lightning, it provides deep learning models, domain-specific loss functions, and reproducibility infrastructure tuned for zero-inflated, heavy-tailed conflict fatality data.

Key Features

🚀 Production-Ready Integration: Seamlessly integrates with the VIEWS pipeline ecosystem via the Genomic Firewall and DNA manifest validation
⚡ Zero-Inflated Data Handling: Specialized loss functions (SpotlightLoss family) and scalers (AsinhTransform chains) purpose-built for conflict fatality distributions
🧠 10 Model Architectures: TFT, N-BEATS, N-HiTS, TiDE, TCN, BlockRNN, Transformer, NLinear, DLinear, TSMixer
📊 Static Covariate Fingerprints: Per-entity conflict statistics (µ, σ, max, trend, sparsity) — implemented in transformers/static_covariates.py but not currently wired into the dataset; see reports/technical_risk_register.md C-36
🔗 Chained Scalers: Arrow-syntax pipelines (AsinhTransform->MaxAbsScaler) for multi-stage feature normalization
🛡️ Fortress Architecture: ADR-governed reproducibility, NaN detection, gradient health monitoring, and training stability callbacks throughout
🧮 GPU Acceleration: Optimized for single- and multi-GPU training via PyTorch Lightning

📦 Installation

git clone https://github.com/views-platform/views-r2darts2.git
cd views-r2darts2
pip install -e .

Requires darts==0.40.0 and pandas 1.x — the newest versions the VIEWS platform can host (its viewser / views-transformation-library dependencies pin pandas<2, views-datafactory caps xarray<2026 and zarr<3). views-pipeline-core>=3.0.0,<4.0.0 is an optional extra: pip install -e ".[manager]". It is imported lazily by DartsForecastingModelManager and by the four ViewsDataset persistence methods (save_predstore, save_appwrite, from_predstore_latest, from_appwrite_latest); everything else works without it. For GPU support, install the appropriate PyTorch version for your CUDA setup first. See the PyTorch installation guide.

🧠 Supported Models

Model	Static Covariates	Description	Ideal For
TFT (Temporal Fusion Transformer)	✅ VSN+GRN gating	Hybrid LSTM + multi-head attention. Variable Selection Networks gate static covariate influence per time step.	Interpretable multivariate forecasting; best when entity identity strongly conditions the forecast.
TSMixer	✅ Concatenation	Alternating time-mixing and feature-mixing MLP blocks. Static covariates are concatenated at each block (no gating).	Large-scale multivariate; fast training. Requires `AsinhTransform->MaxAbsScaler` on static cov stats due to blunt concat.
TiDE	✅ Concatenation	MLP encoder-decoder for long-horizon forecasting. Efficient and scalable.	Resource-constrained environments, long input horizons.
BlockRNN	✅ Concatenation	Stacked RNN/LSTM/GRU with optional static covariate injection.	Sequential dependency modeling; autoregressive forecasting.
NLinear	✅ Concatenation	Lightweight linear model with optional trend/seasonality decomposition.	Baseline modeling; rapid prototyping.
DLinear	✅ Concatenation	Decomposition-Linear — separate linear layers for trend and seasonal components.	Trend/seasonality separation; fast inference.
N-HiTS	❌ Not consumed	Hierarchical interpolation with per-stack pooling→FC→theta pipelines. Flag accepted but not passed to the Darts constructor — the entity-id static covariate is attached but ignored by the model.	Multi-scale temporal pattern extraction; long-term forecasting.
Transformer	❌ Not consumed	Self-attention encoder with positional encoding. Flag accepted but silently ignored — no `use_static_covariates` is passed to the Darts constructor.	Long-range temporal dependency modeling.
N-BEATS	❌ Not supported	Fully-connected stacks with basis expansions for trend/seasonality. Static covariates are architecturally incompatible. Do not configure `use_static_covariates=True`.	Interpretable decomposition; univariate/multivariate forecasting.
TCN (Temporal Convolutional Network)	❌ Not consumed	Dilated causal convolutions with residual connections.	High-frequency series; long-range dependencies.

Important: Models marked ❌ will silently drop static covariate fingerprints even if use_static_covariates=True is set in the config. This is a Darts library constraint, not a bug in this package. On 0.2.x no fingerprint is computed for any model (see Static Covariate Fingerprints below); only the entity id is attached, and for N-HiTS and Transformer even that is never seen by the model weights.

📊 Scalers

Proper data scaling is critical for neural network training. For conflict data — zero-inflated, heavily right-skewed, spanning four orders of magnitude — the choice of transform directly determines whether models converge.

Available Scalers

Scaler	Formula	Best For	Notes
AsinhTransform	$y = \text{asinh}(x)$	Zero-inflated counts, data with negatives	⭐ Recommended for all fatality data. Handles zeros and extreme outliers. Near-linear below 1.
MaxAbsScaler	$y = x / \max(\|x\|)$	Cross-entity normalization after element-wise transform	Maps to $[-1,1]$. Preserves ordinal rank. Required after AsinhTransform for static covariate injection in concatenation-based models.
StandardScaler	$y = (x - \mu)/\sigma$	Roughly normal data	Zero-mean, unit-variance. Poor choice for zero-inflated distributions.
MinMaxScaler	$y = (x - x_{\min})/(x_{\max} - x_{\min})$	Bounded data (0–1, 0–100)	Best for V-Dem indices and WDI percentages.
SqrtTransform	$y = \sqrt{x}$	Moderate skew, count data	Gentler compression than asinh; undefined for negatives.
LogTransform	$y = \log(1 + x)$	Strictly positive skewed data	Undefined for negatives. Prefer AsinhTransform for conflict data.
RobustScaler	Median + IQR	Outlier-heavy data	Resistant to extreme values.
QuantileNormal	Maps to $\mathcal{N}(0,1)$	Any distribution	Forces Gaussian marginal.
QuantileUniform	Maps to $U(0,1)$	Any distribution	Forces uniform marginal.
YeoJohnsonTransform	Power transform	Mixed positive/negative	Makes data more Gaussian-like.

AsinhTransform vs LogTransform

            Log(1+x)       AsinhTransform
x = -50     ❌ undefined    ✓ asinh(-50) = -4.61
x = 0       0.00            0.00
x = 1       0.69            0.88  (non-zero threshold in asinh space: 0.88 ≈ 1 death)
x = 100     4.62            5.30
x = 10000   9.21            9.90

The non_zero_threshold in SpotlightLoss is set to 0.88 because asinh(1) ≈ 0.88 — this exactly corresponds to the boundary of "at least 1 battle death" in raw space.

🔗 Chained Scalers

Use the -> operator to compose transforms sequentially. This is the production standard for all conflict and covariate features:

# Target: suppress zeros + bound output
"target_scaler": "AsinhTransform"

# Features: element-wise compression then cross-entity normalization
"feature_scaler_map": {
    "AsinhTransform->MaxAbsScaler": [
        "lr_splag_1_ged_sb", "lr_ged_ns", "lr_ged_os",
        "lr_acled_sb", "lr_wdi_ny_gdp_mktp_kd",
        # ... all conflict and macro features
    ],
}

# Static covariate statistics — INERT on 0.2.x: forwarded to a forecaster parameter its own
# docstring marks unused; nothing computes the fingerprint (register C-36)
"static_covariate_stats": {"transform": "AsinhTransform->MaxAbsScaler"}

Why MaxAbsScaler after AsinhTransform for features?
AsinhTransform is element-wise: it compresses Syria's ged_sb ≈ 5000 and Chad's ged_sb ≈ 3 independently. After asinh, Syria is at ~8.5 and Chad at ~1.8 — a 4.7× gap persists. MaxAbsScaler maps the entire feature column to [-1, 1] across all 180+ countries, collapsing cross-entity scale while preserving ordinal rank. Without this, concatenation-based models (TSMixer, TiDE) inject raw magnitude bias at every block.

Forward / inverse chain direction:

Forward:  X → Scaler₁.fit_transform(X) → Scaler₂.fit_transform(X') → X_scaled
Inverse:  X_scaled → Scaler₂.inverse_transform → Scaler₁.inverse_transform → X_original

Static Covariate Fingerprints

Designed, not wired (register C-36). The fingerprint below is implemented in views_r2darts2/transformers/static_covariates.py and fully tested, but nothing in the package calls it on 0.2.x — ViewsDataset.to_darts_timeseries attaches only the entity id as a static covariate. The table describes what the module computes, not what models currently receive.

For models that consume static covariates (TFT, TSMixer, TiDE, BlockRNN, NLinear, DLinear), five per-entity statistics are computed from the training partition only (via stat_time_range) and would be injected as TimeSeries.static_covariates metadata:

Statistic	Meaning	Transform Recommendation
`target_mu`	Mean conflict level	`AsinhTransform->MaxAbsScaler`
`target_sigma`	Volatility / spread	`AsinhTransform->MaxAbsScaler`
`target_max`	Peak value (spike extremity)	`AsinhTransform->MaxAbsScaler`
`target_trend`	OLS slope over training window	`AsinhTransform->MaxAbsScaler`
`target_sparsity`	Fraction of zero months	No transform (already in [0,1])

If the module is wired, stat_time_range must be the training window to prevent test-period leakage:

from views_r2darts2.transformers.static_covariates import compute_static_covariates, StaticCovariateConfig

stats = compute_static_covariates(
    time=time_arr, entity=entity_arr, values=values_2d,
    target_columns=["ged_sb"], column_order=column_names,
    stat_time_range=(training_start_month_id, training_end_month_id),
    config=StaticCovariateConfig(transform="AsinhTransform->MaxAbsScaler"),
)

Recommended Scaler by Data Source

Data Source	Feature Type	Recommended Chain	Rationale
UCDP / ACLED	Fatality counts	`AsinhTransform->MaxAbsScaler`	Zero-inflated; cross-entity normalization required
WDI	GDP, population, aid flows	`AsinhTransform->MaxAbsScaler`	Spans many orders of magnitude; can be negative (net migration)
WDI	Percentages (`_zs` suffix)	`AsinhTransform->MaxAbsScaler`	Handles near-zero values; consistent with other features
V-Dem	Democracy indices (0–1 bounded)	`AsinhTransform->MaxAbsScaler` or `MinMaxScaler`	Already bounded; either works
Static cov stats	µ, σ, max, trend	`AsinhTransform->MaxAbsScaler`	Required for concatenation-based models
Static cov stats	Sparsity	None (raw)	Already in [0,1]

⚡ Loss Functions

All loss functions target zero-inflated conflict data: ~90% zeros, ~10% events spanning four orders of magnitude. The loss function family has evolved substantially — the table below shows the current production-recommended functions and the full catalog.

Loss Function Catalog

Loss Function	Status	Base Cell Loss	Key Mechanism	Use When
SpotlightLossLogcosh	⭐ Production	log_cosh	Event-gated shape + event-only level + dead-cell anchor (three components)	Default for all models in production
SpotlightLoss	⭐ Production	Barron(α=1.5)	Same three-component structure on a heavier-tailed base; single gene `non_zero_threshold`; spectral term hard-disabled (`_STFT = False`)	When log_cosh gradient is too aggressive on large errors
PrismLoss	Research	MSE (= MSLE in log space)	KL-DRO + compound weights, no DC/AC decomp, no level anchor	MSLE-aligned optimization without RevIN
SpotlightFocalLoss	Research	log_cosh	Focal weighting by difficulty `(1−exp(−\|e\|))^γ`, no DRO	Models without RevIN; exploration
SentinelLoss	Research	Generalised Charbonnier	Power-law magnitude weights + SiLU symmetry + temporal gradient	Alternative robust base when Barron α needs tuning
WeightedPenaltyHuberLoss	Legacy	Huber	FP/FN multiplicative penalties	Simple baselines; not recommended for production
WeightedHuberLoss	Legacy	Huber	Non-zero reweighting	Simple baselines
TimeAwareWeightedHuberLoss	Legacy	Huber	Temporal decay + event weights	Time-sensitive ablations
TweedieLoss	Legacy	Tweedie (p≈1.5)	Compound Poisson-Gamma	Count data without asinh transform
AsymmetricQuantileLoss	Legacy	Quantile	Asymmetric τ-penalty	When underestimation cost >> overestimation
ZeroInflatedLoss	Legacy	Huber (two-part)	Explicit binary + count split	Explicit zero-inflation modeling
SpikeFocalLoss	Legacy	MSE	Focal on absolute magnitude	Predates KL-DRO; superseded by Spotlight family
ShrinkageLoss	Legacy	Shrinkage	Suppresses easy samples via sigmoid gate	Exploratory; not validated for conflict

SpotlightLossLogcosh — Architecture Deep Dive

The production loss for all current VIEWS models. Operates entirely in asinh space; the target scaler must be AsinhTransform.

Note (2026-09-10): both production classes — views_r2darts2/math/spotlight_loss_logcosh.py and views_r2darts2/math/spotlight_loss.py — are three-component losses with a single gene, non_zero_threshold (LOSS_GENOMES): shape — base cell loss on demeaned per-cell errors, gated by an event mask, DRO-weighted; level — T-scaled loss on the event-only mean gap; anchor — loss on the dead-cell sum. Neither accepts delta; SpotlightLoss carries a spectral branch behind a hard-coded _STFT = False. A configured delta is silently dropped by the genome filter. The five-component design (DC/AC decomposition, compound weights, KL-DRO, level anchor, spectral) described in views_r2darts2/math/README.md is the Gen-5 design lineage; that file's configuration block still advertises delta and is stale (register C-15).

Configuration (SpotlightLossLogcosh):

"loss_function": "SpotlightLossLogcosh",
"non_zero_threshold": 0.88,  # asinh(1) ≈ 0.88 (= 1 battle death in raw space) — the only gene

Loss Function Evolution

The loss function development followed a clear progression as each failure mode was identified and addressed:

Generation	Loss	Problem It Solved	Limitation Discovered
Gen 1	`WeightedPenaltyHuberLoss`	Basic non-zero reweighting	Huber is symmetric; no handling of RevIN bias; manually tuned FP/FN parameters
Gen 2	`TweedieLoss`, `SpikeFocalLoss`	Compound Poisson structure; focal weighting	No DRO; focal exponent γ is a fragile hyperparameter
Gen 3	`PrismLoss`	KL-DRO replaces χ²-DRO; compound weighting is parameter-free	No DC/AC decomposition; RevIN bias accumulates; no level anchor
Gen 4	`SpotlightFocalLoss`	Focal mechanism adapted for regression, no class-specific logic	No DRO; still requires γ tuning
Gen 5	`SpotlightLossLogcosh`	DC/AC decomp + compound + KL-DRO + level anchor (+ spectral, since removed); parameter-free weighting	log_cosh gradient can clip large errors aggressively
Gen 5b	`SpotlightLoss`	Barron(α=1.5) base cell loss — heavier tail than log_cosh	Same three-component structure as current Logcosh; spectral branch disabled

🏗️ Multi-Stack Model Configuration

N-HiTS and TSMixer are multi-stack architectures where residuals are passed between stacks. Layer width ordering is critical and non-obvious.

The Layer Widths Trap

Both N-HiTS and TSMixer use a residual stacking pipeline:

Stack 0 (coarse) → absorbs easy patterns (trend, long cycles)
        ↓ residual
Stack 1 (mid)    → absorbs medium-frequency patterns
        ↓ residual  
Stack 2 (fine)   → must absorb ALL remaining residuals, including spike patterns

The fine stack always has the hardest job. Under SpotlightLoss (DRO weighting), high-conflict country residuals (e.g., Sudan) dominate gradients. If the fine stack has minimal capacity, it cannot model these spikes, producing erratic theta coefficients that cause:

Explosion on high-conflict countries (Sudan, Syria): fine stack theta blows up
Flatline on peaceful countries: coarse stack weights drift toward dominant loss signal, collapsing peaceful predictions to near-zero

Correct configuration:

# WRONG — coarse gets most capacity, fine gets least
"layer_widths": [256, 128, 64]   # ← Sudan explosion + flatline

# CORRECT — fine stack gets most capacity for residual absorption
"layer_widths": [64, 128, 256]   # ← stable, fine stack can handle spikes

N-HiTS Pooling / Frequency Alignment

N-HiTS pools the input sequence before each stack's FC block. The pooling_kernel_sizes and n_freq_downsample must be aligned:

pool_k = 4  →  input compressed to ceil(36/4) = 9 time steps → FC input dim = 9
n_freq = 4  →  theta output has 4 frequency coefficients, interpolated to 36 steps

If pool_k=4 but n_freq=3, there are 9 FC inputs but only 3 theta points — implicit upsampling by 9→36 via 3 basis functions creates a 3:1 gap. This forces the interpolation to guess intermediate values, introducing artificial smoothing that conflicts with spike reconstruction.

Correct alignment:

"pooling_kernel_sizes": [[4, 2, 1]],     # coarse: 9 steps, mid: 18 steps, fine: 36 steps
"n_freq_downsample": [[4, 2, 1]],        # 4 theta / 2 theta / 1 theta, interpolated to 36

The fine stack (pool_k=1, n_freq=1) sees all 36 time steps and produces 36 theta coefficients — effectively identity interpolation. This is correct: the fine stack should not impose any temporal compression on spike signals.

Also use max_pool_1d=True in the coarse stack to preserve spike maxima during pooling (average pooling dilutes spike information that should be routed to the coarse trend stack, not the fine detail stack).

🛡️ Fortress Architecture & Governance

This repository adheres to the Fortress Architecture: strict engineering and mathematical standards designed to guarantee scientific integrity and reproducibility in conflict forecasting.

The repository is governed by:

Architectural Decision Records (ADRs): Sequential, authoritative records of every major design choice.
Class Intent Contracts (CICs): Explicit declarations of purpose and responsibility for every critical class.
Reproducibility Manifest: The mandatory DNA genome that every experiment must declare before execution.
Technical Risk Register: Every known, unfixed concern, tiered and triggered (ADR-014); and the ADR-vs-code disagreements D-01..D-07 (each filed as a GitHub issue, #39–#45; D-05 ruled and closed via #40).
bash docs/validate_docs.sh checks the documentation set against itself and against the code tree — every views_r2darts2/…py or tests/…py path a live doc (this README included) names must exist. It is run by hand, not by CI (register C-55).

Training Stability Callbacks

Every training run is monitored by mandatory Fortress callbacks configured in ModelCatalog (8 of the 14 attached are shown; the others are TrainingStepPatchCallback, ValMetricsCallback, InputBatchMonitorCallback, LossComponentCallback, RichLossDiagnosticsCallback, LossGradientDiagnosticsCallbackV2, plus Lightning's EarlyStopping, LearningRateMonitor and ModelCheckpoint):

Callback	Purpose
`NaNDetectionCallback`	Halts training immediately on NaN in loss or weights
`GradientHealthCallback`	Monitors gradient norm; warns on explosion/vanishing
`WeightNormCallback`	Tracks parameter norm evolution across epochs
`LossStabilityCallback`	Detects loss spikes and plateau regimes
`RevINMonitorCallback`	Monitors RevIN affine parameters for drift
`PredictionSanityCallback`	Validates output shape and value range each epoch
`YHatBarCallback`	Tracks per-series mean predictions (ŷ bar) against targets
`EpochTimingCallback`	Logs wall-clock time per epoch for performance tracking

🔧 API Reference

Most core classes follow the 1-Class-1-File standard; three homogeneous families (dataset/converters.py, dataset/subclasses.py, transformers/static_covariates.py) do not — register D-04 / issue #44.

ScalerSelector

from views_r2darts2.transformers.scaler_selector import ScalerSelector

scaler = ScalerSelector.get_scaler("AsinhTransform")
pipeline = ScalerSelector.instantiate_darts_scaler("AsinhTransform->MaxAbsScaler")  # Darts Pipeline; a single name returns a Darts Scaler

FeatureScalerManager

from views_r2darts2.transformers.feature_scaler_manager import FeatureScalerManager

manager = FeatureScalerManager(
    feature_scaler_map={"AsinhTransform->MaxAbsScaler": ["lr_ged_sb", "lr_ged_ns"]},
    default_scaler=None,
)

Catalogs (Genomic Firewall)

from views_r2darts2.catalogs.model_catalog import ModelCatalog
from views_r2darts2.catalogs.loss_catalog import LossCatalog
from views_r2darts2.catalogs.optimizer_catalog import OptimizerCatalog
from views_r2darts2.catalogs.scheduler_catalog import SchedulerCatalog

# Catalogs validate the DNA manifest on initialization
loss_fn = LossCatalog(config).get_loss()
model   = ModelCatalog(config).get_model("NHiTSModel")

ViewsDataset

from views_r2darts2 import ViewsDataset

# Zarr-backed, lazy; accepts parquet, DataFrame, FeatureFrame, PredictionFrame or Zarr
with ViewsDataset(source="cm_features.parquet", targets=["ged_sb"]) as ds:
    # scalers are fitted on the training window and owned by the dataset
    targets, past_cov = ds.fit_scalers(
        target_scaler="AsinhTransform",
        feature_scaler_map={"AsinhTransform->MaxAbsScaler": ["lr_ged_sb"]},
        time_ids=range(training_start_id, training_end_id + 1),   # fit on the training window only
        return_series=True,
    )
    ts_list = ds.to_darts_timeseries()

The dataset is the single source of truth for all data operations — see docs/CICs/views_dataset.md. For level-specific validation use ViewsDataset.for_loa("cm", source=...).

ReproducibilityGate

from views_r2darts2.infrastructure.reproducibility_gate import ReproducibilityGate

ReproducibilityGate.Config.audit_manifest(config)
ReproducibilityGate.Data.lock_entropy(config["random_state"])   # seeds torch/numpy/random; not full GPU determinism (C-24)
ReproducibilityGate.Temporal.audit_continuity(partition)

📐 Production Configuration Template

Minimal validated configuration for a new model:

def get_hp_config():
    return {
        # Forecast horizon
        "steps": [*range(1, 37)],
        "input_chunk_length": 36,
        "output_chunk_length": 36,
        "output_chunk_shift": 0,

        # Scaling — production standard
        "target_scaler": "AsinhTransform",
        "feature_scaler": None,
        "feature_scaler_map": {
            "AsinhTransform": [
                # All conflict counts, macro indicators, and lagged features
            ],
        },
        "static_covariate_stats": {"transform": "AsinhTransform->MaxAbsScaler"},   # inert on 0.2.x (C-36)

        # Loss — production standard
        "loss_function": "SpotlightLossLogcosh",
        "non_zero_threshold": 0.88, # asinh(1): boundary of 1 battle death

        # Optimizer
        "optimizer_cls": "AdamW",
        "lr": 0.0005,
        "weight_decay": 0.0002,
        "gradient_clip_val": 3,
        "optimizer_kwargs": {
            "betas": (0.9, 0.95), "lr": 0.0005, "weight_decay": 0.0002},

        # Scheduler
        "lr_scheduler_cls": "ReduceLROnPlateau",
        "lr_scheduler_factor": 0.5,
        "lr_scheduler_patience": 12,
        "lr_scheduler_min_lr": 1e-6,
        "lr_scheduler_kwargs": {
            "mode": "min", "factor": 0.5, "patience": 12,
            "min_lr": 1e-6, "cooldown": 3,
            "threshold": 0.01, "threshold_mode": "rel",
        },

        # Training
        "batch_size": 128,
        "n_epochs": 300,
        "early_stopping_patience": 35,
        "early_stopping_min_delta": 0.001,
        "force_reset": True,

        # Normalization
        "use_reversible_instance_norm": True,
        "use_cyclic_encoders": False,
        "use_static_covariates": True,  # Set False for N-BEATS, N-HiTS, Transformer

        # Reproducibility
        "random_state": 67,
        "time_steps": 36,               # not read by views_r2darts2; consumed (if at all) by views-pipeline-core (C-57)
        "rolling_origin_stride": 1,     # not read by views_r2darts2 (C-57)
        "prediction_format": "dataframe",

        # Prediction
        "likelihood": None,
        "num_samples": 1,
        "mc_dropout": False,
        "n_jobs": -1,                   # not read by views_r2darts2 (C-57)
    }

📚 References

Darts: unit8co/darts — Time series forecasting library
VIEWS: viewsforecasting.org — Violence Early-Warning System

Navigation Menu

Repository files navigation

Key Features

📦 Installation

🧠 Supported Models

📊 Scalers

Available Scalers

AsinhTransform vs LogTransform

🔗 Chained Scalers

Static Covariate Fingerprints

Recommended Scaler by Data Source

⚡ Loss Functions

Loss Function Catalog

SpotlightLossLogcosh — Architecture Deep Dive

Loss Function Evolution

🏗️ Multi-Stack Model Configuration

The Layer Widths Trap

N-HiTS Pooling / Frequency Alignment

🛡️ Fortress Architecture & Governance

Training Stability Callbacks

🔧 API Reference

ScalerSelector

FeatureScalerManager

Catalogs (Genomic Firewall)

ViewsDataset

ReproducibilityGate

📐 Production Configuration Template

📚 References

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Key Features

📦 Installation

🧠 Supported Models

📊 Scalers

Available Scalers

AsinhTransform vs LogTransform

🔗 Chained Scalers

Static Covariate Fingerprints

Recommended Scaler by Data Source

⚡ Loss Functions

Loss Function Catalog

SpotlightLossLogcosh — Architecture Deep Dive

Loss Function Evolution

🏗️ Multi-Stack Model Configuration

The Layer Widths Trap

N-HiTS Pooling / Frequency Alignment

🛡️ Fortress Architecture & Governance

Training Stability Callbacks

🔧 API Reference

ScalerSelector

FeatureScalerManager

Catalogs (Genomic Firewall)

ViewsDataset

ReproducibilityGate

📐 Production Configuration Template

📚 References

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