Skip to content

NHITS

NHITS (Neural Hierarchical Interpolation for Time Series) Forecasting.

N-HITS is particularly suitable for hierarchical time series forecasting tasks,

  • where the data can be naturally decomposed into multiple levels of aggregation

  • such as sales data at different geographical levels or product hierarchies

how it works

  • It utilizes a special technique called hierarchical interpolation to efficiently forecast future values. This technique breaks down the forecasting process into smaller components focusing on different frequencies within the data.

  • Additionally, NHITS employs multi-rate input processing, meaning it can analyze data at various granularities to capture both short-term and long-term patterns.

Strengths

  • Accuracy: NHITS boasts high accuracy, especially for short-term forecasting, often outperforming established models like LSTMs.

  • Complex seasonality: It excels at capturing intricate seasonal patterns and trends in time series data.

  • Efficiency: Compared to other high-accuracy models, NHITS can be computationally efficient, requiring less training time in some cases.

  • Flexibility: It can handle multivariate forecasting (multiple time series at once) and incorporate past covariates (additional data points that might influence the forecast).

Weaknesses

  • Data requirements: While generally efficient, NHITS might still require more data for training compared to simpler models.

  • Interpretability: Similar to other neural networks, understanding how NHITS arrives at predictions can be challenging.

help