Fast Adaptive ML for Time-Series Forecasting

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Hi everyone, We have been dealing for a while with the underperformance of Time-Series Machine Learning models (mostly due to regime changes), and haven't found the right library to complete Adaptive Backtesting before the heat-death of the universe. We ended up writing a library from scratch, that comes with an order of magnitude speed-up, called Fold. --- As this is the launch of the core engine of our Forecasting Suite we would love to get some feedback on Fold ( https://github.com/dream-faster/fold )! We’ll be here and happy to answer any questions.

Visit product View on Hacker NewsApr 25, 2023

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: mac, model, models · Missing: agents, macos, agent

40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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