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Chronos: Learning the Language of Time Series

arxiv.org

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Re: Chronos: Learning the Language of Time Series

#11

Post by wenc »

izyda wrote: post #3

I do not have a horse in the race, but it is interesting to see open source comparisons to traditional timeseries strategies: https://github.com/Nixtla/nixtla/tree/main/experiments/amazo... In general, the M-Competitions ( https://forecasters.org/resources/time-series-data/ ), the olympics of timeseries forecasting, have proven frustrating for ML methods... linear models do shockingly well and the ML models that have…

They are comparing a non-ensembled transformer model with an ensemble of simple linear models. It's not surprising that the ensemble models of linear time series models will do well, since ensembles optimize for the bias-variance trade-off.

Transformer/ML models by themselves have a tendency to overfit past patterns. They pick up more signal in the patterns, but they also pick up spurious patterns. They're low bias but high variance.

It would be more interesting to compare an ensemble of transformer models with an ensemble of linear models to see which is more accurate.

(that said, it's pretty impressive that an ensemble of simple linear models can beat a large scale transformer model -- this tells me the domain being forecast has a high degree of variance, which transformer models by themselves don't do well on.)

Re: Chronos: Learning the Language of Time Series

#12

Post by ideamotor »

sampo wrote: post #10
notagoodidea wrote: post #9

Earlier quoted context omitted.

I always worked in R for time series analysis. This cookbook has everything you would need for a plan to analyze a time series [0] and this book provides a strong base and understanding while being focus on forecasting. [1] Have fun ! [0] https://rc2e.com/timeseriesanalysis [1] https://otexts.com/fpp2/

> https://otexts.com/fpp2/ Third edition: https://otexts.com/fpp3/

Agree, great resource.

Re: Chronos: Learning the Language of Time Series

#13

Post by gradascent »

wenc wrote: post #11
izyda wrote: post #3

I do not have a horse in the race, but it is interesting to see open source comparisons to traditional timeseries strategies: https://github.com/Nixtla/nixtla/tree/main/experiments/amazo... In general, the M-Competitions ( https://forecasters.org/resources/time-series-data/ ), the olympics of timeseries forecasting, have proven frustrating for ML methods... linear models do shockingly well and the ML models that have…

They are comparing a non-ensembled transformer model with an ensemble of simple linear models. It's not surprising that the ensemble models of linear time series models will do well, since ensembles optimize for the bias-variance trade-off. Transformer/ML models by themselves have a tendency to overfit past patterns. They pick up more signal in the patterns, but they also pick up spurious patterns. They're low bias b…

fyi I think you have bias and variance the wrong way around. Over-fitting indicates high variance

Re: Chronos: Learning the Language of Time Series

#14

Post by wenc »

gradascent wrote: post #13
wenc wrote: post #11

Earlier quoted context omitted.

They are comparing a non-ensembled transformer model with an ensemble of simple linear models. It's not surprising that the ensemble models of linear time series models will do well, since ensembles optimize for the bias-variance trade-off. Transformer/ML models by themselves have a tendency to overfit past patterns. They pick up more signal in the patterns, but they also pick up spurious patterns. They're low bias b…

fyi I think you have bias and variance the wrong way around. Over-fitting indicates high variance

Thank you for catching that. Corrected.

Re: Chronos: Learning the Language of Time Series

#15

Post by [deleted] »

hbcondo714 wrote: post #7
bhy wrote: post #6

Earlier quoted context omitted.

When you ask what data should be paying attention to, that should be depends on your objective. Do you want to predict something? Identify anomalies? In the end, what matters is understanding the meaning and relations of these data, rather than throwing them in to some ML framework and hoping to get something out.

Prediction and anomalies are not objectives but of the 4 listed, I would say the primary objective is identifying a trend in the data to know whether the data is moving in a specific direction—increasing or decreasing in value. I already added linear regression marks that draws linear regression lines with confidence bands[1] to my Observable plots but they do not give me a “value” so I need to manually look at the g…

[deleted]

Re: Chronos: Learning the Language of Time Series

#16

Post by Galanwe »

hbcondo714 wrote: post #4

Chronos is probably overkill for what I am looking to do with time series data. I just did an Ask HN on time series[0] but unfortunately didn't get the replies I was hoping for. Maybe this thread can get the bump I need: I inherited a large time series JSON dataset in 2024. I've been successful in using the Observable Framework[1] by writing a Rust (rust-script) data loader[2] to parse and plot simple line charts[3]…

Doesn't look like you need anything fancy here.

Load you time serie in a dataframe, and:

> - Variability: how "spread out" are the data points from one another?

So basically df.std(), with rolling variants for short term / long term.

> - Trend: direction of data path, up or down? - Slope: are the data points increasing or decreasing?

Just do a simple rolling linear regression of your data point against time.

Re: Chronos: Learning the Language of Time Series

#18

Post by tomrod »

one_buggy_boi wrote: post #5
izyda wrote: post #3

I do not have a horse in the race, but it is interesting to see open source comparisons to traditional timeseries strategies: https://github.com/Nixtla/nixtla/tree/main/experiments/amazo... In general, the M-Competitions ( https://forecasters.org/resources/time-series-data/ ), the olympics of timeseries forecasting, have proven frustrating for ML methods... linear models do shockingly well and the ML models that have…

Are these models high risk because of their lack of interpratability? Specialized models like temporal fusion transformers attempt to solve this but in practice I'm seeing folks torn apart when defending transformers against model risk committees within organizations that are mature enough to have them.

Interpretability is just one pillar to satisfy in AI governance. You have build submodels to assist with interpreting black box main prediction models.

Re: Chronos: Learning the Language of Time Series

#19

Post by jethkl »

It's great to see research in this field, I know there is opportunity here, and I hope to someday benefit from progress. But I skimmed the paper, and it doesn't appear solve a problem that I have. From the practical standpoint, what I want from a time series tool includes: 1) a small set of simple levers that I can review and tune 2) short training time for any input sets of size O(10k) to O(100k) (this covers seconds/day, minutes/week, hours/year) 3) the process of train + forecast can run fine on CPUs -- not GPUs with low memory overhead 4) decent out of the box performance that basically passes the sniff test and 5) a simple way to include regressors. I've enough experience to have learned to be wary of fully automated tuning, benchmark performance metrics, elaborate models, etc.

Re: Chronos: Learning the Language of Time Series

#20

Post by meow_cat »

Maybe I'm missing something obvious, but what is the idea behind quantizing and tokenizing time series? We tokenize text because text isn't numbers. In the case of time series, we're... turning numbers into less precise numbers? The benefit of scaling and centering is trivial and i guess all timeseries ML does it, but I don't see why we need a token after that.
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