TimeGPT
What is TimeGPT?
TimeGPT is an intriguing project that leverages transformer architecture to predict time series data. Specifically, the developers have trained a variant of ChatGPT on a diverse range of historical data, including information on bids, exchange rates, and other metrics.
While access to the model remains restricted, the author managed to gain access to a closed beta, allowing them to test TimeGPT’s capabilities. It’s essential to note that any forecasts generated by the model should not be interpreted as financial recommendations, as neural networks have a propensity for both imaginative extrapolation and invention. Instead, the model’s utility lies in its predictive abilities, as exemplified by the provided forecasts based on data such as cash volume (M2) and key interest rates.
Understanding TimeGPT
The dataset utilized for this experiment incorporated data dating back to 2013, and the resulting forecasts showcased TimeGPT’s potential to make predictions under different scenarios. For example, it presented two forecasts: one under the assumption that current cash volume and key interest rates remain unchanged for an entire year, and another where these factors are considered alongside the model’s own predictions.
→ Moderate is if the current cash volume and the key rate will not change the whole next year
→ Considered is a forecast where M2 indicators and the key rate are taken into account, but they are also predicted by the same model, separate requests.
Eventually:
-In January 2025, for $ 1, the United States will be asked either 105.76 rubles or 157.58 rubles, according to TimegPT-1
-In January 2025, the key rate of the Central Bank will be either ~ 10% or ~ 17.5%, according to TimegPT-1
The model’s predictions suggest varying outcomes for the exchange rate and key interest rate in January 2025, underscoring its ability to navigate through complex data. While TimeGPT may not account for all external factors, it still demonstrates a significant leap in performance, particularly during the course of March 2024. Please be mindful that working with these datasets was a demanding task, and, as always, it’s essential to approach AI-generated forecasts with caution.
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About The Author
Damir is the team leader, product manager, and editor at Metaverse Post, covering topics such as AI/ML, AGI, LLMs, Metaverse, and Web3-related fields. His articles attract a massive audience of over a million users every month. He appears to be an expert with 10 years of experience in SEO and digital marketing. Damir has been mentioned in Mashable, Wired, Cointelegraph, The New Yorker, Inside.com, Entrepreneur, BeInCrypto, and other publications. He travels between the UAE, Turkey, Russia, and the CIS as a digital nomad. Damir earned a bachelor's degree in physics, which he believes has given him the critical thinking skills needed to be successful in the ever-changing landscape of the internet.
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Damir is the team leader, product manager, and editor at Metaverse Post, covering topics such as AI/ML, AGI, LLMs, Metaverse, and Web3-related fields. His articles attract a massive audience of over a million users every month. He appears to be an expert with 10 years of experience in SEO and digital marketing. Damir has been mentioned in Mashable, Wired, Cointelegraph, The New Yorker, Inside.com, Entrepreneur, BeInCrypto, and other publications. He travels between the UAE, Turkey, Russia, and the CIS as a digital nomad. Damir earned a bachelor's degree in physics, which he believes has given him the critical thinking skills needed to be successful in the ever-changing landscape of the internet.
