v0.1.3 - Covariates / Joint forecasting

TimesFmForecaster.ForecastJoint takes several series at once, any of which may carry
values known into the future.

• Series are attended jointly and normalised per variate, so a price in the thousands
  mixes with a flag in {0,1} without scaling. The ceiling is read from the checkpoint
  rather than hardcoded — MaxVariates reports what the loaded one states, 32 for 3.0.
• There is no variate identity or ordering. The model sees anonymous series and infers
  their relationship from co-movement in the supplied window.
• A known-future variate cannot be the forecast target; marking it is refused, since that
  would hand a series its own future.
• The known future is gathered by rolling whole patches forward, so the context must be a
  positive multiple of the patch length and leave at least one patch after it. Both are
  validated rather than producing a silently wrong anchor.
• samples/GroceryCovariates reproduces the scenario from Google's covariates notebook:
  ice cream and sunscreen, temperature and promotion known for both weeks. The numbers are
  not comparable: the notebook uses the XReg path, which regresses covariates on forecast
  residuals, whereas 3.0 attends over them as variates.

Only Tsfm.Forecasting.TimesFm changed. Other packages republished to keep project reference management simple.

TimesFM checkpoints are non-commercially licensed and are not distributed with any
package; see NOTICE.

Packages

Package
Tsfm.Forecasting	safetensors, checkpoint abstraction (MIT)
Tsfm.Forecasting.Kronos	tokenizer, model, forecaster (MIT)
Tsfm.Forecasting.Kronos.Weights.Small	Kronos-small (24.7M) + Tokenizer-base
Tsfm.Forecasting.Kronos.Weights.Mini	Kronos-mini (4.1M) + Tokenizer-2k
Tsfm.Forecasting.TimesFm	TimesFM 3.0 inference, no weights (Apache-2.0)

Full Changelog: v0.1.2...v0.1.3