PATCHTSTS is a modern approach to the analysis and forecasting of time series based on the architecture of transformers. Unlike traditional algorithms - classic statistical models, recurrent neural networks and boosting methods - transformers allow you to catch long -term dependencies and nonlinear patterns of a temporary signal.
patchtst is especially effective in situations when a large amount of data with severe seasonality is analyzed-for example, with daily, weekly or monthly cycles. This architecture is suitable for tasks, which requires high forecast accuracy, such as financial calculations, industrial analytics or energy system planning.
Why is the approach based on transformers is important
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The best activation of long sequences
Transformers use the mechanism of attention, which allows you to effectively analyze both short and long data intervals, without “forgetting” the previously processed context.
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Parallel data processing
Unlike RNN, the architecture of transformers immediately processes all fragments of the input sequence, which accelerates training and processing.
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Flexibility and adaptability
The ability to adapt to the various scales of patches (sections of a row), improving the quality of forecasts on data with different frequency and structure.
How Patchtst works
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Data preparation: The temporary series is divided into "patches"-fixed areas that become elements of the model input.
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Encoder on Transformers: Each patch is processed in parallel, attention mechanisms reveal key dependencies inside and between the areas.
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Decoder: At the output, the model collects the forecast, combining information from all fragments.
The best activation of long sequences
Transformers use the mechanism of attention, which allows you to effectively analyze both short and long data intervals, without “forgetting” the previously processed context.
Parallel data processing
Unlike RNN, the architecture of transformers immediately processes all fragments of the input sequence, which accelerates training and processing.
Flexibility and adaptability
The ability to adapt to the various scales of patches (sections of a row), improving the quality of forecasts on data with different frequency and structure.
Data preparation: The temporary series is divided into "patches"-fixed areas that become elements of the model input.
Encoder on Transformers: Each patch is processed in parallel, attention mechanisms reveal key dependencies inside and between the areas.
Decoder: At the output, the model collects the forecast, combining information from all fragments.
This approach is especially effective when working with data where small seasonal changes inside of long trends are important-for example, electric consumption, capital market, IOT sensors.
