neuralforecast
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Scalable and user friendly neural :brain: forecasting algorithms.
Feature: - Adds [MOMENT ](https://arxiv.org/pdf/2402.03885) to NeuralForecast, following [this implementation](https://github.com/moment-timeseries-foundation-model/moment/blob/main/moment/models/moment.py#L127). It is implemented (following the reference implementation) as a univariate model that does not support exogenous variables. Todo: - Testing...
Add the VCformer model, a multivariate forecasting model that uses a Variable Correlation attention mechanism and implements a Koopman Temporal Detector module. [Paper](https://arxiv.org/abs/2405.11470) [Code](https://github.com/CSyyn/VCformer)
This is a large refactoring PR and open for discussion. The main goal of the PR is to unify API across different model types, and unify loss functions across different...
WIP:
# Rationale * Users have asked about whether we can modify the default learning rate scheduler behaviors. See https://github.com/Nixtla/neuralforecast/issues/852#issuecomment-1961923861 and https://nixtlacommunity.slack.com/archives/C031M8RLC66/p1713516602654109 * Instead of adding arguments to all the models,...
### What happened + What you expected to happen The `predict` method fails with the following error when the model has been trained on multi-gpu with `ddp_spawn` strategy: `TypeError: vstack():...
### What happened + What you expected to happen I was attempting to follow the example for automatic hyperparameter tuning described here: https://nixtlaverse.nixtla.io/neuralforecast/examples/automatic_hyperparameter_tuning.html#4-optuna-backend The example fails and returns this error...
### Description Request to Add TimeMixer Model to neuralforecast Project ### Use case Dear Maintainer, I hope this message finds you well. I am writing to you regarding your excellent...
### What happened + What you expected to happen When performing transfer learning if we have historic scalers we should refit them to the new data, such that when applying...