# Nixtla

## Docs

- [Differences](https://nixtla-old-docs.mintlify.app/coreforecast/differences.md)
- [Expanding](https://nixtla-old-docs.mintlify.app/coreforecast/expanding.md)
- [Exponentially weighted](https://nixtla-old-docs.mintlify.app/coreforecast/exponentially_weighted.md)
- [Grouped array](https://nixtla-old-docs.mintlify.app/coreforecast/grouped_array.md)
- [coreforecast](https://nixtla-old-docs.mintlify.app/coreforecast/index.md): Fast implementations of common forecasting routines
- [Lag transforms](https://nixtla-old-docs.mintlify.app/coreforecast/lag_transforms.md)
- [Rolling](https://nixtla-old-docs.mintlify.app/coreforecast/rolling.md)
- [Scalers](https://nixtla-old-docs.mintlify.app/coreforecast/scalers.md)
- [Seasonal](https://nixtla-old-docs.mintlify.app/coreforecast/seasonal.md)
- [Favorita](https://nixtla-old-docs.mintlify.app/datasetsforecast/favorita.html.md)
- [Hierarchical Datasets](https://nixtla-old-docs.mintlify.app/datasetsforecast/hierarchical.html.md)
- [datasetsforecast](https://nixtla-old-docs.mintlify.app/datasetsforecast/index.html.md)
- [Long-Horizon Datasets](https://nixtla-old-docs.mintlify.app/datasetsforecast/long_horizon.html.md): Download and wrangling utility for long-horizon datasets.
- [Long-Horizon Original Datasets](https://nixtla-old-docs.mintlify.app/datasetsforecast/long_horizon2.html.md): Download and wrangling utility for long-horizon datasets. These datasets have been used by `NHITS, AutoFormer, Informer, PatchTST, TiDE` among many other neural forecasting methods. The datasets include the original [ETTh1, ETTh2, ETTm1, ETTm2, Weather, ILI, TrafficL](https://github.com/zhouhaoyi/ET…
- [M3 dataset](https://nixtla-old-docs.mintlify.app/datasetsforecast/m3.html.md): Download the M3 dataset.
- [M4 dataset](https://nixtla-old-docs.mintlify.app/datasetsforecast/m4.html.md): Download and evaluate the M4 dataset.
- [M5 dataset](https://nixtla-old-docs.mintlify.app/datasetsforecast/m5.html.md): Download and evaluate the M5 dataset.
- [PHM2008 dataset](https://nixtla-old-docs.mintlify.app/datasetsforecast/phm2008.html.md): Download the PHM2008 dataset.
- [Datasets Utils](https://nixtla-old-docs.mintlify.app/datasetsforecast/utils.html.md)
- [Bootstrap](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/australiandomestictourism-bootstraped-intervals.html.md)
- [Normality](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/australiandomestictourism-intervals.html.md)
- [PERMBU](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/australiandomestictourism-permbu-intervals.html.md)
- [Geographical Aggregation (Tourism)](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/australiandomestictourism.html.md): Geographical Hierarchical Forecasting on Australian Tourism Data
- [Geographical and Temporal Aggregation (Tourism)](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/australiandomestictourismcrosstemporal.html.md): Geographical and Temporal Hierarchical Forecasting on Australian Tourism Data
- [Temporal Aggregation (Tourism)](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/australiandomestictourismtemporal.html.md): Temporal Hierarchical Forecasting on Australian Tourism Data
- [Geographical Aggregation (Prison Population)](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/australianprisonpopulation.html.md): Geographical Hierarchical Forecasting on Australian Prison Population Data
- [Install](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/installation.html.md): Install HierachicalForecast with pip or conda
- [Introduction](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/introduction.html.md): Introduction to Hierarchial Forecasting using `HierarchialForecast`
- [Local vs Global Temporal Aggregation](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/localglobalaggregation.html.md): Temporal Hierarchical Aggregation on a local or global level.
- [Temporal Aggregation with THIEF](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/m3withthief.html.md): Temporal Hierarchical Forecasting on M3 monthly and quarterly data with THIEF
- [Neural/MLForecast](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/mlframeworksexample.html.md)
- [Non-Negative MinTrace](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/nonnegativereconciliation.html.md)
- [Probabilistic Forecast Evaluation](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/tourismlarge-evaluation.html.md): Hierarchical Forecast's reconciliation and evaluation.
- [Quick Start](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/tourismsmall.html.md): Minimal Example of Hierarchical Reconciliation
- [Quick Start (Polars)](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/examples/tourismsmallpolars.html.md): Minimal Example of Hierarchical Reconciliation using Polars
- [Hierarchical Forecast 👑](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/index.html.md)
- [Core](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/src/core.html.md)
- [Hierarchical Evaluation](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/src/evaluation.html.md)
- [Reconciliation Methods](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/src/methods.html.md)
- [Probabilistic Methods](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/src/probabilistic_methods.html.md)
- [Aggregation/Visualization Utils](https://nixtla-old-docs.mintlify.app/hierarchicalforecast/src/utils.html.md)
- [Auto](https://nixtla-old-docs.mintlify.app/mlforecast/auto.html.md)
- [Callbacks](https://nixtla-old-docs.mintlify.app/mlforecast/callbacks.html.md)
- [Core](https://nixtla-old-docs.mintlify.app/mlforecast/core.html.md)
- [Distributed Forecast](https://nixtla-old-docs.mintlify.app/mlforecast/distributed.forecast.html.md): Distributed pipeline encapsulation
- [DaskLGBMForecast](https://nixtla-old-docs.mintlify.app/mlforecast/distributed.models.dask.lgb.html.md): dask LightGBM forecaster
- [DaskXGBForecast](https://nixtla-old-docs.mintlify.app/mlforecast/distributed.models.dask.xgb.html.md): dask XGBoost forecaster
- [RayLGBMForecast](https://nixtla-old-docs.mintlify.app/mlforecast/distributed.models.ray.lgb.html.md): ray LightGBM forecaster
- [RayXGBForecast](https://nixtla-old-docs.mintlify.app/mlforecast/distributed.models.ray.xgb.html.md): ray XGBoost forecaster
- [SparkLGBMForecast](https://nixtla-old-docs.mintlify.app/mlforecast/distributed.models.spark.lgb.html.md): spark LightGBM forecaster
- [SparkXGBForecast](https://nixtla-old-docs.mintlify.app/mlforecast/distributed.models.spark.xgb.html.md): spark XGBoost forecaster
- [End to end walkthrough](https://nixtla-old-docs.mintlify.app/mlforecast/docs/getting-started/end_to_end_walkthrough.html.md): Detailed description of all the functionalities that MLForecast provides.
- [Install](https://nixtla-old-docs.mintlify.app/mlforecast/docs/getting-started/install.html.md): Instructions to install the package from different sources.
- [Quick start (distributed)](https://nixtla-old-docs.mintlify.app/mlforecast/docs/getting-started/quick_start_distributed.html.md): Minimal example of distributed training with MLForecast
- [Quick start (local)](https://nixtla-old-docs.mintlify.app/mlforecast/docs/getting-started/quick_start_local.html.md): Minimal example of MLForecast
- [Analyzing the trained models](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/analyzing_models.html.md): Access and interpret the models after fitting
- [Cross validation](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/cross_validation.html.md): In this example, we'll implement time series cross-validation to evaluate model's performance.
- [Custom date features](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/custom_date_features.html.md): Define your own functions to be used as date features
- [Custom training](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/custom_training.html.md): Customize the training procedure for your models
- [Exogenous features](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/exogenous_features.html.md): Use exogenous regressors for training and predicting
- [Hyperparameter optimization](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/hyperparameter_optimization.html.md): Tune your forecasting models
- [Lag transformations](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/lag_transforms_guide.html.md): Compute features based on lags
- [MLflow](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/mlflow.html.md): Log your metrics and models
- [One model per step](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/one_model_per_horizon.html.md): Train one model to predict each step of the forecasting horizon
- [Predict callbacks](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/predict_callbacks.html.md): Get access to the input features and predictions in each forecasting horizon
- [Predicting a subset of ids](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/predict_subset.html.md): Compute predictions for only a subset of the training ids
- [Probabilistic forecasting](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/prediction_intervals.html.md): In this example, we'll implement prediction intervals
- [Sample weights](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/sample_weights.html.md): Provide a column to pass through to the underlying models as sample weights
- [Using scikit-learn pipelines](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/sklearn_pipelines.html.md): Leverage scikit-learn's composability to define pipelines as models
- [Target transformations](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/target_transforms_guide.html.md): Seamlessly transform target values
- [Training with numpy arrays](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/training_with_numpy.html.md): Convert your dataframes to arrays to use less memory and train faster
- [Transfer Learning](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/transfer_learning.html.md)
- [Transforming exogenous features](https://nixtla-old-docs.mintlify.app/mlforecast/docs/how-to-guides/transforming_exog.html.md): Compute transformations on your exogenous features for MLForecast
- [Electricity Load Forecast](https://nixtla-old-docs.mintlify.app/mlforecast/docs/tutorials/electricity_load_forecasting.html.md): In this example we will show how to perform electricity load forecasting using MLForecast alongside many models. We also compare them against the prophet library.
- [Detect Demand Peaks](https://nixtla-old-docs.mintlify.app/mlforecast/docs/tutorials/electricity_peak_forecasting.html.md): In this example we will show how to perform electricity load forecasting on the ERCOT (Texas) market for detecting daily peaks.
- [Prediction intervals](https://nixtla-old-docs.mintlify.app/mlforecast/docs/tutorials/prediction_intervals_in_forecasting_models.html.md)
- [Feature engineering](https://nixtla-old-docs.mintlify.app/mlforecast/feature_engineering.html.md): Compute transformations on exogenous regressors
- [MLForecast](https://nixtla-old-docs.mintlify.app/mlforecast/forecast.html.md): Full pipeline encapsulation
- [MLForecast 🤖](https://nixtla-old-docs.mintlify.app/mlforecast/index.html.md): **mlforecast** is a framework to perform time series forecasting using machine learning models, with the option to scale to massive amounts of data using remote clusters.
- [Lag transforms](https://nixtla-old-docs.mintlify.app/mlforecast/lag_transforms.html.md): Built-in lag transformations
- [LightGBMCV](https://nixtla-old-docs.mintlify.app/mlforecast/lgb_cv.html.md): Time series cross validation with LightGBM.
- [Optimization](https://nixtla-old-docs.mintlify.app/mlforecast/optimization.html.md)
- [Target transforms](https://nixtla-old-docs.mintlify.app/mlforecast/target_transforms.html.md)
- [Utils](https://nixtla-old-docs.mintlify.app/mlforecast/utils.html.md)
- [Hyperparameter Optimization](https://nixtla-old-docs.mintlify.app/neuralforecast/common.base_auto.html.md): Machine Learning forecasting methods are defined by many hyperparameters that control their behavior, with effects ranging from their speed and memory requirements to their predictive performance. For a long time, manual hyperparameter tuning prevailed. This approach is time-consuming, **automated h…
- [NN Modules](https://nixtla-old-docs.mintlify.app/neuralforecast/common.modules.html.md)
- [TemporalNorm](https://nixtla-old-docs.mintlify.app/neuralforecast/common.scalers.html.md): Temporal normalization has proven to be essential in neural forecasting tasks, as it enables network's non-linearities to express themselves. Forecasting scaling methods take particular interest in the temporal dimension where most of the variance dwells, contrary to other deep learning techniques l…
- [Core](https://nixtla-old-docs.mintlify.app/neuralforecast/core.html.md): NeuralForecast contains two main components, PyTorch implementations deep learning predictive models, as well as parallelization and distributed computation utilities. The first component comprises low-level PyTorch model estimator classes like `models.NBEATS` and `models.RNN`. The second component…
- [Cross-validation](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/capabilities/cross_validation.html.md)
- [Exogenous Variables](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/capabilities/exogenous_variables.html.md)
- [Hyperparameter Optimization](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/capabilities/hyperparameter_tuning.html.md)
- [Optimization Objectives](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/capabilities/objectives.html.md)
- [Forecasting Models](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/capabilities/overview.html.md)
- [Predict Insample](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/capabilities/predictinsample.html.md): Tutorial on how to produce insample predictions.
- [Save and Load Models](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/capabilities/save_load_models.html.md)
- [Time Series Scaling](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/capabilities/time_series_scaling.html.md)
- [Data Requirements](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/getting-started/datarequirements.html.md): Dataset input requirments
- [Installation](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/getting-started/installation.html.md): Install NeuralForecast with pip or conda
- [About NeuralForecast](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/getting-started/introduction.html.md): **NeuralForecast** offers a large collection of neural forecasting models focused on their usability, and robustness. The models range from classic networks like `MLP`, `RNN`s to novel proven contributions like `NBEATS`, `NHITS`, `TFT` and other architectures.
- [Quickstart](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/getting-started/quickstart.html.md): Fit an LSTM and NHITS model
- [Adding Models to NeuralForecast](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/adding_models.html.md): Tutorial on how to add new models to NeuralForecast
- [Statistical, Machine Learning and Neural Forecasting methods](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/comparing_methods.html.md): In this notebook, you will make forecasts for the M5 dataset choosing the best model for each time series using cross validation.
- [Uncertainty quantification with Conformal Prediction](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/conformal_prediction.html.md): Tutorial on how to train neuralforecast models and obtain prediction intervals using the conformal prediction methods
- [Cross-validation](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/cross_validation.html.md): Implement cross-validation to evaluate models on historical data
- [Distributed Training](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/distributed_neuralforecast.html.md)
- [Explainability for Deep Learning Forecasting Models](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/explainability.md)
- [Forecasting with TFT: Temporal Fusion Transformer](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/forecasting_tft.html.md)
- [End to End Walkthrough](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/getting_started_complete.html.md): Model training, evaluation and selection for multiple time series
- [Hierarchical Forecast](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/hierarchical_forecasting.html.md)
- [Intermittent Data](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/intermittent_data.html.md): In this notebook, we'll implement models for intermittent or sparse data using the M5 dataset.
- [Interpretable Decompositions](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/interpretable_decompositions.html.md)
- [Using Large Datasets](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/large_datasets.html.md): Tutorial on how to train neuralforecast models on datasets that cannot fit into memory
- [Long-Horizon Forecasting with NHITS](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/longhorizon_nhits.html.md)
- [Long-Horizon Probabilistic Forecasting](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/longhorizon_probabilistic.html.md)
- [Long-Horizon Forecasting with Transformer models](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/longhorizon_transformers.html.md): Tutorial on how to train and forecast Transformer models.
- [Multivariate Forecasting with TSMixer](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/multivariate_tsmixer.html.md): Tutorial on how to do multivariate forecasting using TSMixer models.
- [Robust Forecasting](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/robust_forecasting.html.md)
- [Temporal Classification](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/temporal_classification.html.md)
- [Transfer Learning](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/transfer_learning.html.md)
- [Probabilistic Forecasting](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/uncertainty_quantification.html.md): Quantify uncertainty
- [Using MLflow](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/tutorials/using_mlflow.html.md): Log your neuralforecast experiments to MLflow
- [Detect Demand Peaks](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/use-cases/electricity_peak_forecasting.html.md): In this example we will show how to perform electricity load forecasting on the ERCOT (Texas) market for detecting daily peaks.
- [Predictive Maintenance](https://nixtla-old-docs.mintlify.app/neuralforecast/docs/use-cases/predictive_maintenance.html.md)
- [NumPy Evaluation](https://nixtla-old-docs.mintlify.app/neuralforecast/losses.numpy.html.md): NeuralForecast contains a collection NumPy loss functions aimed to be used during the models' evaluation.
- [PyTorch Losses](https://nixtla-old-docs.mintlify.app/neuralforecast/losses.pytorch.html.md): NeuralForecast contains a collection PyTorch Loss classes aimed to be used during the models' optimization.
- [Autoformer](https://nixtla-old-docs.mintlify.app/neuralforecast/models.autoformer.html.md)
- [BiTCN](https://nixtla-old-docs.mintlify.app/neuralforecast/models.bitcn.html.md)
- [DeepAR](https://nixtla-old-docs.mintlify.app/neuralforecast/models.deepar.html.md)
- [DeepNPTS](https://nixtla-old-docs.mintlify.app/neuralforecast/models.deepnpts.html.md)
- [Dilated RNN](https://nixtla-old-docs.mintlify.app/neuralforecast/models.dilated_rnn.html.md)
- [DLinear](https://nixtla-old-docs.mintlify.app/neuralforecast/models.dlinear.html.md)
- [FEDformer](https://nixtla-old-docs.mintlify.app/neuralforecast/models.fedformer.html.md)
- [GRU](https://nixtla-old-docs.mintlify.app/neuralforecast/models.gru.html.md)
- [HINT](https://nixtla-old-docs.mintlify.app/neuralforecast/models.hint.html.md)
- [AutoModels](https://nixtla-old-docs.mintlify.app/neuralforecast/models.html.md): NeuralForecast contains user-friendly implementations of neural forecasting models that allow for easy transition of computing capabilities (GPU/CPU), computation parallelization, and hyperparameter tuning.
- [Informer](https://nixtla-old-docs.mintlify.app/neuralforecast/models.informer.html.md)
- [iTransformer](https://nixtla-old-docs.mintlify.app/neuralforecast/models.itransformer.html.md)
- [KAN](https://nixtla-old-docs.mintlify.app/neuralforecast/models.kan.html.md)
- [LSTM](https://nixtla-old-docs.mintlify.app/neuralforecast/models.lstm.html.md)
- [MLP](https://nixtla-old-docs.mintlify.app/neuralforecast/models.mlp.html.md): One of the simplest neural architectures are Multi Layer Perceptrons (`MLP`) composed of stacked Fully Connected Neural Networks trained with backpropagation. Each node in the architecture is capable of modeling non-linear relationships granted by their activation functions. Novel activations like R…
- [MLPMultivariate](https://nixtla-old-docs.mintlify.app/neuralforecast/models.mlpmultivariate.html.md): One of the simplest neural architectures are Multi Layer Perceptrons (`MLP`) composed of stacked Fully Connected Neural Networks trained with backpropagation. Each node in the architecture is capable of modeling non-linear relationships granted by their activation functions. Novel activations like R…
- [NBEATS](https://nixtla-old-docs.mintlify.app/neuralforecast/models.nbeats.html.md)
- [NBEATSx](https://nixtla-old-docs.mintlify.app/neuralforecast/models.nbeatsx.html.md)
- [NHITS](https://nixtla-old-docs.mintlify.app/neuralforecast/models.nhits.html.md)
- [NLinear](https://nixtla-old-docs.mintlify.app/neuralforecast/models.nlinear.html.md)
- [PatchTST](https://nixtla-old-docs.mintlify.app/neuralforecast/models.patchtst.html.md)
- [Reversible Mixture of KAN - RMoK](https://nixtla-old-docs.mintlify.app/neuralforecast/models.rmok.html.md)
- [RNN](https://nixtla-old-docs.mintlify.app/neuralforecast/models.rnn.html.md)
- [SOFTS](https://nixtla-old-docs.mintlify.app/neuralforecast/models.softs.html.md)
- [StemGNN](https://nixtla-old-docs.mintlify.app/neuralforecast/models.stemgnn.html.md)
- [TCN](https://nixtla-old-docs.mintlify.app/neuralforecast/models.tcn.html.md)
- [TFT](https://nixtla-old-docs.mintlify.app/neuralforecast/models.tft.html.md)
- [TiDE](https://nixtla-old-docs.mintlify.app/neuralforecast/models.tide.html.md): Time-series Dense Encoder (`TiDE`) is a MLP-based univariate time-series forecasting model. `TiDE` uses Multi-layer Perceptrons (MLPs) in an encoder-decoder model for long-term time-series forecasting. In addition, this model can handle exogenous inputs.
- [Time-LLM](https://nixtla-old-docs.mintlify.app/neuralforecast/models.timellm.html.md)
- [TimeMixer](https://nixtla-old-docs.mintlify.app/neuralforecast/models.timemixer.html.md)
- [TimesNet](https://nixtla-old-docs.mintlify.app/neuralforecast/models.timesnet.html.md)
- [TimeXer](https://nixtla-old-docs.mintlify.app/neuralforecast/models.timexer.html.md)
- [TSMixer](https://nixtla-old-docs.mintlify.app/neuralforecast/models.tsmixer.html.md): Time-Series Mixer (`TSMixer`) is a MLP-based multivariate time-series forecasting model. `TSMixer` jointly learns temporal and cross-sectional representations of the time-series by repeatedly combining time- and feature information using stacked mixing layers. A mixing layer consists of a sequential…
- [TSMixerx](https://nixtla-old-docs.mintlify.app/neuralforecast/models.tsmixerx.html.md): Time-Series Mixer exogenous (`TSMixerx`) is a MLP-based multivariate time-series forecasting model, with capability for additional exogenous inputs. `TSMixerx` jointly learns temporal and cross-sectional representations of the time-series by repeatedly combining time- and feature information using s…
- [Vanilla Transformer](https://nixtla-old-docs.mintlify.app/neuralforecast/models.vanillatransformer.html.md)
- [xLSTM](https://nixtla-old-docs.mintlify.app/neuralforecast/models.xlstm.md)
- [PyTorch Dataset/Loader](https://nixtla-old-docs.mintlify.app/neuralforecast/tsdataset.html.md): Torch Dataset for Time Series
- [Example Data](https://nixtla-old-docs.mintlify.app/neuralforecast/utils.html.md): The `core.NeuralForecast` class allows you to efficiently fit multiple `NeuralForecast` models for large sets of time series. It operates with pandas DataFrame `df` that identifies individual series and datestamps with the `unique_id` and `ds` columns, and the `y` column denotes the target time seri…
- [Contribute to Nixtla](https://nixtla-old-docs.mintlify.app/statsforecast/docs/contribute/contribute.md)
- [Nixtla Documentation](https://nixtla-old-docs.mintlify.app/statsforecast/docs/contribute/docs.md)
- [Understanding Issue Labels](https://nixtla-old-docs.mintlify.app/statsforecast/docs/contribute/issue-labels.md)
- [Submit an Issue 📢](https://nixtla-old-docs.mintlify.app/statsforecast/docs/contribute/issues.md)
- [Step by step](https://nixtla-old-docs.mintlify.app/statsforecast/docs/contribute/step-by-step.md)
- [Techstack](https://nixtla-old-docs.mintlify.app/statsforecast/docs/contribute/techstack.md)
- [Dask](https://nixtla-old-docs.mintlify.app/statsforecast/docs/distributed/dask.html.md): Run StatsForecast distributedly on top of Dask.
- [Ray](https://nixtla-old-docs.mintlify.app/statsforecast/docs/distributed/ray.html.md): Run StatsForecast distributedly on top of Ray.
- [Spark](https://nixtla-old-docs.mintlify.app/statsforecast/docs/distributed/spark.html.md): Run StatsForecast distributedly on top of Spark.
- [Amazon Forecast vs StatsForecast](https://nixtla-old-docs.mintlify.app/statsforecast/docs/experiments/amazonstatsforecast.html.md): Amazon's AutoML vs open source statistical methods
- [AutoARIMA Comparison (Prophet and pmdarima)](https://nixtla-old-docs.mintlify.app/statsforecast/docs/experiments/autoarima_vs_prophet.html.md)
- [Forecasting at Scale using ETS and ray (M5)](https://nixtla-old-docs.mintlify.app/statsforecast/docs/experiments/ets_ray_m5.html.md): Forecast the M5 dataset
- [StatsForecast ETS and Facebook Prophet on Spark (M5)](https://nixtla-old-docs.mintlify.app/statsforecast/docs/experiments/prophet_spark_m5.html.md): This notebook was originally executed using DataBricks
- [End to End Walkthrough](https://nixtla-old-docs.mintlify.app/statsforecast/docs/getting-started/getting_started_complete.html.md): Model training, evaluation and selection for multiple time series
- [End to End Walkthrough with Polars](https://nixtla-old-docs.mintlify.app/statsforecast/docs/getting-started/getting_started_complete_polars.html.md): Model training, evaluation and selection for multiple time series
- [Quick Start](https://nixtla-old-docs.mintlify.app/statsforecast/docs/getting-started/getting_started_short.html.md): Minimal Example of StatsForecast
- [Install](https://nixtla-old-docs.mintlify.app/statsforecast/docs/getting-started/installation.html.md): Install StatsForecast with pip or conda
- [Automatic Time Series Forecasting](https://nixtla-old-docs.mintlify.app/statsforecast/docs/how-to-guides/automatic_forecasting.html.md): How to do automatic forecasting using `AutoARIMA`, `AutoETS`, `AutoCES` and `AutoTheta`.
- [Exogenous Regressors](https://nixtla-old-docs.mintlify.app/statsforecast/docs/how-to-guides/exogenous.html.md): In this notebook, we'll incorporate exogenous regressors to a StatsForecast model.
- [Generating features](https://nixtla-old-docs.mintlify.app/statsforecast/docs/how-to-guides/generating_features.html.md): Leverage StatsForecast models to create features
- [Migrating from R](https://nixtla-old-docs.mintlify.app/statsforecast/docs/how-to-guides/migrating_R.md)
- [Numba caching](https://nixtla-old-docs.mintlify.app/statsforecast/docs/how-to-guides/numba_cache.html.md): Enabling caching for numba functions to reduce cold-starts
- [Sklearn models](https://nixtla-old-docs.mintlify.app/statsforecast/docs/how-to-guides/sklearn_models.html.md): Use any scikit-learn model for forecasting
- [ADIDA Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/adida.html.md)
- [ARCH Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/arch.html.md)
- [ARIMA Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/arima.html.md)
- [AutoARIMA Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/autoarima.html.md)
- [AutoCES Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/autoces.html.md)
- [AutoETS Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/autoets.html.md)
- [AutoRegressive Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/autoregressive.html.md)
- [AutoTheta Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/autotheta.html.md)
- [CrostonClassic Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/crostonclassic.html.md)
- [CrostonOptimized Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/crostonoptimized.html.md)
- [CrostonSBA Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/crostonsba.html.md)
- [Dynamic Optimized Theta Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/dynamicoptimizedtheta.html.md)
- [Dynamic Standard Theta Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/dynamicstandardtheta.html.md)
- [GARCH Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/garch.html.md)
- [Holt Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/holt.html.md)
- [Holt Winters Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/holtwinters.html.md)
- [IMAPA Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/imapa.html.md)
- [MFLES](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/mfles.html.md)
- [Multiple Seasonal Trend (MSTL)](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/multipleseasonaltrend.html.md)
- [Optimized Theta Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/optimizedtheta.html.md)
- [Seasonal Exponential Smoothing Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/seasonalexponentialsmoothing.html.md)
- [Seasonal Exponential Smoothing Optimized Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/seasonalexponentialsmoothingoptimized.html.md)
- [Simple Exponential Smoothing Optimized Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/simpleexponentialoptimized.html.md)
- [Simple Exponential Smoothing Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/simpleexponentialsmoothing.html.md)
- [Standard Theta Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/standardtheta.html.md)
- [TSB Model](https://nixtla-old-docs.mintlify.app/statsforecast/docs/models/tsb.html.md)
- [Anomaly Detection](https://nixtla-old-docs.mintlify.app/statsforecast/docs/tutorials/anomalydetection.html.md): In this notebook, we'll implement anomaly detection in time series data
- [Conformal Prediction](https://nixtla-old-docs.mintlify.app/statsforecast/docs/tutorials/conformalprediction.html.md): In this example, we'll implement conformal prediction
- [Cross validation](https://nixtla-old-docs.mintlify.app/statsforecast/docs/tutorials/crossvalidation.html.md): In this example, we'll implement time series cross-validation to evaluate model's performance.
- [Electricity Load Forecast](https://nixtla-old-docs.mintlify.app/statsforecast/docs/tutorials/electricityloadforecasting.html.md): In this example we will show how to perform electricity load forecasting considering a model capable of handling multiple seasonalities (MSTL).
- [Detect Demand Peaks](https://nixtla-old-docs.mintlify.app/statsforecast/docs/tutorials/electricitypeakforecasting.html.md): In this example we will show how to perform electricity load forecasting on the ERCOT (Texas) market for detecting daily peaks.
- [Volatility forecasting (GARCH & ARCH)](https://nixtla-old-docs.mintlify.app/statsforecast/docs/tutorials/garch_tutorial.html.md): In this example, we'll forecast the volatility of the S&P 500 and several publicly traded companies using GARCH and ARCH models
- [Intermittent or Sparse Data](https://nixtla-old-docs.mintlify.app/statsforecast/docs/tutorials/intermittentdata.html.md): In this notebook, we'll implement models for intermittent or sparse data
- [MLFlow](https://nixtla-old-docs.mintlify.app/statsforecast/docs/tutorials/mlflow.html.md): Run Statsforecast with MLFlow.
- [Multiple seasonalities](https://nixtla-old-docs.mintlify.app/statsforecast/docs/tutorials/multipleseasonalities.html.md): In this example we will show how to forecast data with multiple seasonalities using an MSTL.
- [Statistical, Machine Learning and Neural Forecasting methods](https://nixtla-old-docs.mintlify.app/statsforecast/docs/tutorials/statisticalneuralmethods.html.md): In this notebook, you will make forecasts for the M5 dataset choosing the best model for each time series using cross validation.
- [Probabilistic Forecasting](https://nixtla-old-docs.mintlify.app/statsforecast/docs/tutorials/uncertaintyintervals.html.md): In this example, we'll implement prediction intervals
- [StatsForecast ⚡️](https://nixtla-old-docs.mintlify.app/statsforecast/index.html.md): StatsForecast offers a collection of popular univariate time series forecasting models optimized for high performance and scalability.
- [Core Methods](https://nixtla-old-docs.mintlify.app/statsforecast/src/core/core.html.md): Methods for Fit, Predict, Forecast (fast), Cross Validation and plotting
- [FugueBackend](https://nixtla-old-docs.mintlify.app/statsforecast/src/core/distributed.fugue.html.md)
- [Models](https://nixtla-old-docs.mintlify.app/statsforecast/src/core/models.html.md): Models currently supported by StatsForecast
- [StatsForecast's Models](https://nixtla-old-docs.mintlify.app/statsforecast/src/core/models_intro.md)
- [Feature engineering](https://nixtla-old-docs.mintlify.app/statsforecast/src/feature_engineering.html.md): Generate features for downstream models
- [Data](https://nixtla-old-docs.mintlify.app/utilsforecast/data.html.md): Utilies for generating time series datasets
- [Evaluation](https://nixtla-old-docs.mintlify.app/utilsforecast/evaluation.html.md): Model performance evaluation
- [Feature engineering](https://nixtla-old-docs.mintlify.app/utilsforecast/feature_engineering.html.md): Create exogenous regressors for your models
- [utilsforecast](https://nixtla-old-docs.mintlify.app/utilsforecast/index.html.md): Forecasting utilities
- [Losses](https://nixtla-old-docs.mintlify.app/utilsforecast/losses.html.md): Loss functions for model evaluation.
- [Plotting](https://nixtla-old-docs.mintlify.app/utilsforecast/plotting.html.md): Time series visualizations
- [Preprocessing](https://nixtla-old-docs.mintlify.app/utilsforecast/preprocessing.html.md): Utilities for processing data before training/analysis

## Optional

- [TimeGPT](https://nixtla.io/docs)