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Hi! Max from Nixtla here. Those are users of our open-source libraries. nixtlaverse.nixtla.io, not users of TimeGPT.

Hi, Max from Nixtla here. We are surprised that this has gained so much attention and are excited about both the positive and critical responses. Some important clarifications: The…

We love ARIMAs. That is why we put so much effort into creating fast and scalable Arimas and AutoArima in Python [1]. Regarding your valid concern. There are several reasons for th…

Using 30,490 series of daily sales at Walmart (M5) we show that, Amazon Forecast is 60% less accurate and 669 times more expensive than running an open-source alternative in a simp…

A lot of M3 datasets we use are high-frequency, with large seasonal inputs. Considering Gaussian Processes (GP) complexity is O(N^3), a careful study of their performance would be …

Bonferroni's correction on hold-out data is an excellent suggestion. To adapt it into time series forecasting, one could perform temporal cross-validation with rolling windows and …

Thanks for the comment! In Machine Learning literature, the variance of accuracy measurements originates from different network parameters initialization. Since the deep learning e…

Comparison of several Deep Learning models and ensembles to classical statistical models for the 3,003 series of the M3 competition.

Imagine you want to forecast the next day of electricity load. We ran the test with PJM data. Results: SeasonalNaive is 20% more accurate than NeuralProphet and 366 times faster. S…

In the tutorial, we show how to Forecast 1M Time Series in 15 Minutes with Spark, Fugue, and Nixtla’s Statsforecast.