Combining lightweight design with high forecasting accuracy
Many DL forecasting models tend to follow AI trends.
But not TSMixer. Released by Google almost a year ago, it has gained significant traction and is widely used as an alternative to traditional ML-based models, thanks to these advantages:
- Lightweight: Its MLP-based architecture efficiently captures patterns in both time and feature dimensions.
- Multi-Purpose: The TSMixer-Ext variant accommodates historical data, future known inputs, and static exogenous variables.
- Multi-Channel Modeling: It leverages cross-variate information — its feature-mixing MLPs enable joint learning of interdependencies across covariates.
- Superior Long-Term Forecasting: Capable of handling longer contexts, it excels at forecasting horizons up to 720 data points, as benchmarks show.
These characteristics make TSMixer a versatile choice for domains like demand planning, retail, and financial markets.
Let’s get started!
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