Francis is a well-known and established technology industry expert and thought leader. He has a proven track record of helping clients develop impactful, pragmatic and effective business and product strategies and messaging based on in depth analysis of the current technology, business, economic and geopolitical environment and trends.
According to IBM , attention is not all you need when forecasting certain outcomes with generative AI. You also need time. Earlier this year, IBM made its open-source TinyTimeMixer model available on Hugging Face under an Apache License. Based on IBM ’s Granite foundation model, TTM is a lightweight pre-trained time series foundation model for time-series forecasting based on a patch-mixer architecture for learning context and correlations across time and multiple variables.
One common factor between the different types of models is the need for massive amounts of data to train the models properly. Language and vision foundation models have essentially the entirety of the internet at their disposal. TSFMs, however, require very specific time-stamped data that is typically not publicly available. Some estimates go as high as 95% of this type of data is still proprietary and not publicly available.
Since its release in April 2024, TTM has had over one million downloads from Hugging Face, which begs the question: What time series applications are developers using IBMs Granite TTM for? According to IBM, TTM is being used for a variety of value-added, multi-variable use cases. One use is forecasting flash storage device performance across over 350 key performance indicators.
Time Series Foundation Model Generative AI IBM Hugging Face Granite Model AI
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