[HN Gopher] Google's 200M-parameter time-series foundation model...
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Google's 200M-parameter time-series foundation model with 16k
context
Author : codepawl
Score : 285 points
Date : 2026-03-31 05:21 UTC (17 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| Foobar8568 wrote:
| Somehow I missed that one. Are there any competition on this?
|
| I always had difficulties with ML and time series, I'll need to
| try that out.
| rockwotj wrote:
| https://www.datadoghq.com/blog/datadog-time-series-foundatio...
|
| https://moment-timeseries-foundation-model.github.io/
|
| https://arxiv.org/abs/2403.07815
|
| A friend at work used one to predict when our CEO would post in
| Slack, which is verry entertaining to see if correct.
| Foobar8568 wrote:
| Many thanks for the links!
| chwzr wrote:
| there is TabPFN [1] which also has time series capabilities.
|
| [1] https://priorlabs.ai/tabpfn
| _1 wrote:
| https://www.opentslm.com/
| bitshiftfaced wrote:
| There are some other transformer based models on the GIFT
| leaderboard: https://huggingface.co/spaces/Salesforce/GIFT-Eval
| EmilStenstrom wrote:
| Here is the link to the blogpost, that actually describe what
| this is: https://github.com/google-research/timesfm?tab=readme-
| ov-fil...
| refulgentis wrote:
| That takes me to the same content as the submission, a GitHub
| repo (Chrome on iOS)
| rockwotj wrote:
| Probably the better link:
| https://research.google/blog/a-decoder-only-foundation-
| model...
| akshayshah wrote:
| And https://arxiv.org/pdf/2310.10688 if you want the full
| paper.
| Cyuonut wrote:
| I suppose they tried to link this:
| https://research.google/blog/a-decoder-only-foundation-
| model...
| nels wrote:
| I think you meant to link this page:
| https://research.google/blog/a-decoder-only-foundation-model...
| OliverGuy wrote:
| Wish they gave some numbers for total GPU hours to train this
| model, seems comparatively tiny when compared to LLMs so
| interested to know how close this is to something trainable by
| your average hobbyist/university/small lab
| OliverGuy wrote:
| Edit, it looks like the paper does
|
| TPUv5e with 16 tensor cores for 2 days for the 200M param
| model.
|
| Claude reckons this is 60 hours on a 8xA100 rig, so very
| accessibile compared to LLMs for smaller labs
| EmilStenstrom wrote:
| I somehow find the concept of a general time series model
| strange. How can the same model predict egg prices in Italy, and
| global inflation in a reliable way?
|
| And how would you even use this model, given that there are no
| explanations that help you trust where the prediction comes
| from...
| teruakohatu wrote:
| What is not generally understood is that these models don't
| predict egg prices or inflation in Italy.
|
| They decompose a time series into trends, seasonality and
| residuals. That's what they are actually modelling.
|
| They cannot predict wars in the Middle East influencing
| inflation unless there is a seasonal pattern(s).
| visarga wrote:
| ARIMA and ARMA models
| d--b wrote:
| The main issue is that people do use them to predict bitcoin
| prices intraday and that sort of things.
| nico wrote:
| Is it an issue because it works, or because it doesn't? Or
| because it's bitcoin?
|
| I genuinely want to know. Thank you
| d--b wrote:
| It is an issue because bitcoin is highly unpredictable.
|
| These tools are good at predicting timeseries that are in
| fact quite predictable. Like insurances will use this to
| estimate the number of people who will die from cancer in
| the next year, the year after that, and so on up to 50
| years in the future. The model will extrapolate the
| progresses made in cancer treatment from the current
| trend, etc. It is a prediction, cause it's still possible
| that a breakthrough comes in and suddenly people don't
| die from a certain form of cancer, but generally it
| should be roughly correct.
|
| Bitcoin prices are a lot more chaotic, influenced by a
| ton of unrelated events that shape its path a certain
| way. There is absolutely no certainty that studying the
| shape of its past evolution will help in any way
| understand its future evolution.
|
| Of course here I mean by studying its price alone. If you
| add more information, like who's behind each trend and
| why, you have a much better sense of what could happen
| next.
| cybrox wrote:
| Wars in the middle east seem to have increasingly regular
| patterns tied to stock market opening hours, unfortunately.
| rubyn00bie wrote:
| I totally agree with the sentiment but from what I can
| tell, I'd say they tend happen immediately before or after
| markets open _and_ close. Essentially, and to their
| maximum, screwing absolutely everyone who isn't in the
| clique from participating in the trade.
|
| FWIW-- the only sure fire way to win the trade is to buy
| time and assume both gross incompetence and negligence when
| it comes action. The only caveat is if the markets tank
| enough, this administration will signal capitulation before
| hand, e.g. Trump mildly capitulating on tariffs last April
| after the markets proceed to relentlessly defecate
| themselves.
|
| 0-DTE options are typically, and for good reason, stupid
| gambles. But, right now they can't even be considered
| gambling, because there's zero chance of winning. Not just
| bad odds, but no odds. Again just signaling how truly
| malicious this admin is and its disdain for anyone and
| everyone not close to them.
| jofzar wrote:
| I mean it's super obvious, it's directly tied to scrubs
| popularity.
|
| New season of scrubs = new war in the middle east.
| FartyMcFarter wrote:
| Wow, I didn't know. Thank you! Such a great show.
| jofzar wrote:
| It's suprisingly good, like it's it's 100% worth watching
| if you liked scrubs.
| perks_12 wrote:
| I am not familiar with time series models, but judging from
| your answer, it would be necessary to feed long time series
| into this model for it to detect trends. What is a token
| here? Can it, for the lack of a better example, take in all
| intraday movements of a stock for a day, a week, a month,
| etc?
| teruakohatu wrote:
| I tend to avoid time series forecasting when I can help it
| because I find it hard to communicate to stakeholders that
| a neural network (or another method) is not an oracle.
|
| If you are talking about granularity of observations, it
| would depend on what you are trying to predict (the price
| in an hour or the price in 12 months?) and how quickly you
| need the prediction (100ms? Tomorrow morning?). If I had
| infinite data I would use granularity as a hyper parameter
| and tune that to a level that produced the best test
| results.
|
| I am for example currently using weekly averages for non-
| price data forecasting. I could use daily data but weekly
| is absolutely adequate for this purpose.
| ghywertelling wrote:
| You can use lightgbm with appropriate feature
| engineering.
| ReptileMan wrote:
| It is the Middle East. Wars are always in season. And supply
| is more than the demand.
| lordgrenville wrote:
| That's what traditional time-series modelling does. This is a
| foundational model, which means it's just a neural network
| trained on lots of time series. (So maybe OP's question still
| stands? But it's the same question as "how can LLMs be good
| at so many different kinds of conversations?")
| dist-epoch wrote:
| Because traditional time-series modelling (ARIMA, GARCH,
| ...) is too "simple" and "strict". Just like "simple"
| computer vision (OpenCV, edge-detection, ...) was crushed
| by neural networks when having to deal with real world
| images.
| robot-wrangler wrote:
| This seemed like a good answer at first. But on further
| thought, images on the whole really do seem to have quite
| a bit more standard structure / "grammar" to exploit
| compared to arbitrary time-series. Many images are of the
| world, where there is gravity so you might see
| preponderance of blobs at the bottom, or the repetitive
| types like people, animals, faces, eyes. Wildly abstract
| images still have some continuity, pixels in a
| neighborhood are likely to be similar.
|
| Time series _in general_ have none of this kind of
| structure that 's strictly necessary. I'm sure that many
| real-world sensors typically have some gaussian
| distribution aspects + noise and/or smoothness and
| locality types of assumptions that are pretty safe, but
| presumably that simple stuff is exactly what traditional
| time-series modelling was exploiting.
|
| Maybe the real question is just what kind of time-series
| are in the training data, and why do we think whatever
| implicit structure that is there actually generalizes? I
| mean, you can see how any training that mixes pictures of
| dogs and cats with picturing of people could maybe
| improve drawing hair, detecting hair, or let you draw
| people AND dogs. It's less clear to me how mixing sensor
| data / financial data / anything else together could be
| helpful.
| dist-epoch wrote:
| > It's less clear to me how mixing sensor data /
| financial data / anything else together could be helpful.
|
| Because many of these have the same underlying causal
| structures - humans doing things, weather correlations,
| holidays.
|
| Well studied behavioral stuff like "the stock market
| takes the stairs up and the elevator down" which is not
| really captured by "traditional" modelling tools.
|
| I'm sure people will be doing mechanical interpretation
| on these models to extract what they pattern match for
| prediction.
| torginus wrote:
| Personally, coming from an EE background and not finance
| or statistics, I would go about identifying these
| patterns with an Signals & Systems toolbox, like systems
| identification, various matched filters/classifiers.
|
| This might be a totall wrong approach, but I think it
| might make sense to try to model a matched filter based
| on previous stock selloff/bullrun trigger events, and
| then see if the it has any predictive ability, likewise
| the market reaction seems to be usually some sort of
| delayed impulse-like activity, with the whales reacting
| quickly, and then a distribution of less savvy investors
| following up the signal with various delays.
|
| I'm sure other smarter people have explored this approach
| much more in depth before me.
| esafak wrote:
| You're crafting features. The modern approach to ML (deep
| learning) is to use over-parameterized models and let
| them learn the features. Perhaps you remember this?
| https://www.nytimes.com/2012/06/26/technology/in-a-big-
| netwo...
| srean wrote:
| Except that their success in the time series domain has
| been rather lackluster and elusive. It will s one of the
| few domains where old school models are not only less
| work to maintain but also more accurate. There are a few
| exceptions here and there. Every year there are a few
| neural nets based challengers. You can follow the M
| series of computations from its start to see this
| evolution.
| robot-wrangler wrote:
| Maybe because useful time-series modeling is usually
| _really_ about causal modeling? My understanding is that
| mediated causality in particular is still very difficult,
| where adding extra hops in the middle takes CoT
| performance from like 90% to 10%.
| srean wrote:
| Yes causal models are hard.
|
| NNs do ok on those time series problems where it is
| really about learning a function directly off time. This
| is nonlinear regression where time is just another input
| variable.
|
| Cases where one has to adjust for temporaly correlated
| errors, those seem to be harder for NNs. BTW I am talking
| about accuracies beyond what a typical RNN variants will
| achieve, which is pretty respectable. It's the case that
| more complicated DNNs don't seem to do much better
| inspite of their significant model complexity.
| orangemaen wrote:
| LightGBM won M5 and it wasn't even a competition.
| srean wrote:
| The task was slightly different and favored GBMs. Note
| they aren't NNs whose underwhelming performance was what
| my comment was about.
|
| The M series of competitions change the tasks every year
| to explore what models perform best under different
| scenarios. As I mentioned, neural network based models
| win here and there, but very spotty performance over all.
| robot-wrangler wrote:
| > Because many of these have the same underlying causal
| structures - humans doing things, weather correlations,
| holidays.
|
| Or, you know, maybe they aren't. Thermometers and photon
| counts are related to weather sometimes, but not
| holidays. Holidays are related to traffic sensors and to
| markets, but not Geiger counters.
|
| > Well studied behavioral stuff like "the stock market
| takes the stairs up and the elevator down" which is not
| really captured by "traditional" modelling tools.
|
| Prices are the opposite, up like a shot during shocks,
| falling slowly like a feather. So that particular pattern
| seems like a great example of over-fitting danger and why
| you wouldn't expect mixing series of different types to
| be work very well.
| dist-epoch wrote:
| Electricity demand is influenced very strongly by
| holidays, strongly by weather and from weak to strong by
| geopolitics (depending on location).
|
| The model will have a library of patterns, and will be
| able to pattern match subtle ones to deduce "this time
| series has the kind of micro-patterns which appear in
| strongly weather influenced time-series", and use this to
| activate the weather pattern cluster.
|
| To use your example, when served thermometer data, the
| model notices that the holiday pattern cluster doesn't
| activate/match at all, and will ignore it.
|
| And then it makes sense to train it on the widest
| possible time series, so it can build a vast library of
| patterns and find correlations of activation between
| them.
| energy123 wrote:
| Sometimes you want inductive bias. No universally true
| claim can be made like this.
| graemep wrote:
| Do these models predict on just a single time series then?
|
| it is far more useful for predictions to look for
| correlations between time series. This is far more complex
| than looking for correlations in general because most time
| series trend up or down and therefore correlate.
| amelius wrote:
| What makes these models different from models used for e.g.
| audio?
|
| Or other low-dimensional time domain signals?
| carschno wrote:
| You could abstract speech or other audio as a series of
| sounds, where time is indeed a factor. Speech, however, has
| patterns that are more similar to written language than to
| seasonal patterns that are typically assumed in time
| series. While trained on different data, the architecture
| of TimesFM is actually similar to LLMs. But not identical,
| as pointed out at https://research.google/blog/a-decoder-
| only-foundation-model...:
|
| > Firstly, we need a multilayer perceptron block with
| residual connections to convert a patch of time-series into
| a token that can be input to the transformer layers along
| with positional encodings (PE).
|
| > [...]
|
| > Secondly, at the other end, an output token from the
| stacked transformer can be used to predict a longer length
| of subsequent time-points than the input patch length,
| i.e., the output patch length can be larger than the input
| patch length.
| amelius wrote:
| If "seasonal patterns" is the thing that differentiates
| between these two data sources, then perhaps time series
| models should be called seasonal models?
| jcelerier wrote:
| > They cannot predict wars in the Middle East influencing
| inflation unless there is a seasonal pattern(s).
|
| well...
| guntars wrote:
| The Middle East war season is upon us once again
| Forgeties79 wrote:
| Born too soon to deploy to the Middle East.
|
| Born too late to deploy to the Middle East.
|
| Born just in time to deploy to the Middle East.
| morkalork wrote:
| Next you'll suggest something looney like a correlation
| with the 11-year solar cycle!
|
| (for those who are lost:
| https://x.com/onionweigher/status/1936630237208469898)
| a-dub wrote:
| ar(k) stuff, sure. that's old news. i would expect the
| newfangled stuff to be good at 0-shot learning of pre-event
| signatures spread across multiple series, at a minimum.
| benob wrote:
| I would say:
|
| - decomposition: discover a more general form of Fourrier
| transform to untangle the underlying factors
|
| - memorization: some patterns are recurrent in many domains
| such as power low
|
| - multitask: exploit cross-domain connections such as weather
| vs electricity
| lovelearning wrote:
| My understanding is that the synthetic training data helps
| capture abstract time-series patterns that are common in all
| domains.
|
| As they say in appendix 8:
|
| > We create the synthetic data to reflect common time-series
| patterns using traditional statistical models. We start with
| four simple times series patterns:
|
| > * Piece-wise linear trends (I), where the number of the
| piece-wise linear components is randomly chosen between 2 and
| 8.
|
| > * ARMA(p, q) (II), where 1 <= p, q <= 8 and the corresponding
| coefficients are generated from either a multivariate Gaussian
| or a uniform, then normalized.
|
| > * Seasonal patterns. In particular we create the sine (III)
| and the cosine (IV) waves of different random periods between 4
| and max context length / 2 time-points and time delays.
|
| If there were no such underlying patterns in the class of all
| time-series data, then even the idea of traditional time-series
| models would be fundamentally misplaced.
|
| And since this is a transformer model, it also looks for
| patterns in the problem-specific input data at inference time,
| just like how the input context to an LLM influences its
| output's relevance.
| eru wrote:
| > How can the same model predict egg prices in Italy, and
| global inflation in a reliable way?
|
| How can the same lossy compression algorithm (eg JPG) compress
| pictures of everything in a reliable way?
| cenamus wrote:
| It can't compress pictures of everything in a reliable way.
|
| Text and anything with lots of high frequency components
| looks terrible
| at_compile_time wrote:
| Reliably terrible.
| eru wrote:
| It still doesn't pretty well on text. And we have newer
| formats and ideas that would also deal with that. (To be
| really dead simple: have a minimal container format that
| decides between png or jpg, use png for text.)
|
| However: white noise is where it really struggles. But real
| pictures of the real world don't look like white noise.
| Even though in some sense white noise is the most common
| type of picture a priori.
|
| Similar for real world time series: reality mostly doesn't
| look like white noise.
| FartyMcFarter wrote:
| White noise is random, so it's incompressible by
| definition. By JPG or by any other method no matter how
| clever.
| eru wrote:
| I have a very peculiar coin. With 1% probability it turns
| up heads and with 99% probability it turns up tails.
|
| A string of flips is random, but it's very compressible.
|
| In any case, my point was that reality ain't uniformly
| random. And not only that: pretty much anything you can
| point your camera at shares enough similarity in their
| distribution that we pretty much have universal
| compression algorithms for real world data.
| hamdingers wrote:
| What you're saying is only true for lossless compression,
| if you're fine discarding data you can compress anything.
| Try it yourself: magick -size 512x512
| xc:gray +noise Random noise.png magick noise.png
| -interlace Plane -quality 75 compressed_noise.jpg
|
| Result is ~380k smaller and doesn't look much different
| at 100%.
| annie511266728 wrote:
| It's not really predicting "egg prices" or "inflation" -- it's
| mostly fitting patterns that happen to show up in those series.
|
| The problem isn't domain generalization, it's that we keep
| pretending these models have any notion of what the data means.
|
| People ask how one model can understand everything, but that
| assumes there's any understanding involved at all.
|
| At some point you have to ask: how much of "forecasting" is
| actually anything more than curve fitting with better
| marketing?
| fjdjshsh wrote:
| "curve-fitting" has a long history (centuries old) and could
| be regarded more as a numerical method issue.
|
| Rigorous understanding of what is over fitting, techniques to
| avoid it and select the right complexity of the model, etc,
| are much newer. This is a statistical issue.
|
| My point is that forecasting isn't curve fitting, even
| thought curve fitting is one element of it.
| JackeJR wrote:
| Actually it can. See
| https://youtu.be/FUQwijSDzg8?si=LWd5gVNYRd3HH9rJ
|
| Or just search for the James-Stein paradox.
| thesz wrote:
| > How can the same model predict egg prices in Italy, and
| global inflation in a reliable way?
|
| For one, there's Benford's law:
| https://en.wikipedia.org/wiki/Benford%27s_law
|
| So, predict sign (branch predictors in modern CPUs also use
| neural networks of sorts), exponent (most probably it changes
| slowly) and then predict mantissa using Benford's law.
| samuelknight wrote:
| I think that a model designed to ignore semantic chatter like
| financial news and deeply inspect the raw data is a very
| powerful perspective.
| ludicrousdispla wrote:
| It's best to think of it as a giant tree, from which you can
| pick cherries.
| strongpigeon wrote:
| When I worked on Google Ads, we used time series forecasting to
| compute the odds of an ad campaign reaching its goal (and to
| tell users how likely they were to hit them).
|
| A ton of (unsophisticated) advertisers would just draw a line
| from zero to the number they are at today and project that line
| to the end of the month to forecast the amount of
| conversions/spend they were going to hit. This of course
| doesn't take into account various seasonalities (day-of-week,
| time-of-year, etc.) and gives you a pretty poor forecast.
| Compared to those, time-series forecasting is much more
| accurate.
|
| Is it perfectly accurate? No, that's impossible. But when you
| can train a model on all advertising campaigns, you can give
| good 95% confidence intervals.
| wiradikusuma wrote:
| Also: https://github.com/Nixtla/nixtla and
| https://facebook.github.io/prophet/
| mijailt wrote:
| And https://github.com/amazon-science/chronos-forecasting
| ra wrote:
| This has been around a few months now, has anyone built anything
| on it?
| jdthedisciple wrote:
| Let me be blunt: Shannon would tell us that time forecasting is
| bullshit:
|
| There is infinitely more entropy in the real world out there than
| any model can even remotely capture.
|
| The world is not minecraft.
| mikkom wrote:
| Yeah all weather forecasts are just magic
| eru wrote:
| And JPG doesn't work either..
| kgwgk wrote:
| Whether forecasting is simple: it either rains or it doesn't.
| 50/50 probability!
| tgv wrote:
| Weather forecasts are notoriously iffy, and accuracy drops
| with time, but we understand the physics behind it (to a
| large extent). There's also a lot of fine-grained data
| available. For some arbitrary time series, there's only one
| data sequence, and the model is unknown. Extrapolation then
| becomes a lot more magical.
| FartyMcFarter wrote:
| > Shannon would tell us that time forecasting is bullshit
|
| If you're trying to forecast random data, then yes, it's
| bullshit. Otherwise you have a chance.
| GTP wrote:
| But, if you don't have the information required for a
| forecast, then the outcome can look random. We know the
| physics needed to predict the outcome of a dice throw, but,
| since to predict the outcome you would need a lot of
| information that you don't have, the output is random to you.
| drzaiusx11 wrote:
| Time series forecasting has proven useful in a number of
| different domains from weather to health monitoring. Sure you
| can easily over fit on the training data, but in general that's
| a data source/input problem where you need many high quality
| data sources to find the signal in the noise.
|
| The world is chaotic sure, but there are still truths to be
| found in noisy time series data; saying that the world is too
| random to be knowable is a bit dismissive, no?
| jdthedisciple wrote:
| I agree when it comes to highly niche applications with a
| generous SNR.
|
| Universal models though?
|
| And I haven't even mentioned the fact that en mass
| forecasting ITSELF may influence the subject of forecasting.
| bwfan123 wrote:
| > time forecasting is bullshit
|
| for a model to be useful, it doesnt need to capture the
| behavior of a system. It only needs to capture signals which
| can be useful. For example, for a biased coin toss, a model is
| already useful if it can predict a little better than random.
| dash2 wrote:
| So the time series are provided with no context? It's just
| trained on lots of sets of numbers? Then you give it a new set of
| numbers and it guesses the rest, again with no context?
|
| My guess as to how this would work: the machine will first guess
| from the data alone if this is one of the categories it has
| already seen/inferred (share prices, google trend cat searches
| etc.) Then it'll output a plausible completion for the category.
|
| That doesn't seem as if it will work well for any categories
| outside the training data. I would rather just use either a
| simple model (ARIMA or whatever) or a theoretically-informed
| model. But what do I know.
| Tarq0n wrote:
| If it works for predicting the next token in a very long stream
| of tokens, why not. The question is what architecture and
| training regimen it needs to generalize.
| raghavMultilipi wrote:
| This has been around a few months now, has anyone built anything
| on it?
| magimas wrote:
| we did some internal tests. The quality isn't bad, it works
| quite well. But it's essentially on the same level of an ARIMA
| model trained on the data just much bigger and slower.
|
| So in my opinion it currently falls into a kind of void. If
| your use case is worth predicting and you put a data scientist
| on it, you're better off just training cheaper ARIMA models.
| clarionbell wrote:
| That is disappointing. One would say that with all the budget
| and compute, Google would be able to create something that
| beats methods from 70s. Maybe we are hitting some hard
| limits.
|
| Maybe it would be better to train an LLM with various tuning
| methodologies and make a dedicated ARIMA agent. You throw in
| data, some metadata and requested window of forecast. Out
| comes parameters for "optimal" conventional model.
| magimas wrote:
| I think this could be an interesting read for you, I read
| it last week and it kind of argues the same points:
| https://shakoist.substack.com/p/against-time-series-
| foundati...
| SpaceManNabs wrote:
| thanks for sharing.
|
| i met an associate working for a particular VC and they
| were really into time series foundational models. I
| argued the most of the "Why real forecasting problems
| break the whole frame" as to why they were wasting their
| time at that time.
|
| she was totally convinced i was wrong because she was
| discussing investing with some top and well respected
| researchers that were really pushing this and wanted to
| make a startup around it.
|
| i was and am still confused as at all the wishful
| thinking. then again, sometimes the best time to sell an
| idea is right before you think it is possible.
| emsign wrote:
| Can this finally break the stock markets?
| GTP wrote:
| The safe bet is no. Based on other comments, this would depend
| a lot on the specific trends you're trying to predict. But it
| wouldn't work for everything in the stock market.
| kuu wrote:
| It would be nice to add (2024) to the title, this is not news
| (see: https://research.google/blog/a-decoder-only-foundation-
| model...)
| mrklol wrote:
| Not directly 2024, there was a big update end 2025
| croemer wrote:
| (2024)
| pplonski86 wrote:
| Can someone explain ELI5 how it does work? and how many data
| points it can read?
| konschubert wrote:
| Let's say I have long time series of past solar irradiation and
| long time series of past weather forecasts. Can this model make
| use of weather forecasts for time X in the _future_ to predict
| electricity prices in the future?
|
| That is, can it use one time series at time X to predict another
| time series at time X?
|
| Or is this strictly about finding patterns WITHIN a time series.
| etrautmann wrote:
| The paper suggests it's for forecasting. How this doesn't just
| represent the relatively small number of training samples isn't
| obvious to me. If most of the time series for training go up
| and to the right then I assume that's what the model will
| (generally) do, but who knows.
| htrp wrote:
| isn't this basically prophet?
| staticautomatic wrote:
| No. Prophet is based on curve-fitting.
| mikert89 wrote:
| I'm willing to bet an intelligent LLM with a dataset and a pandas
| stats package could outperform this model by running its own
| experiments and making predictions
| doruk101 wrote:
| Instead of willing to bet, you can do it yourself and prove it.
| It is not like there is a ceiling for doing what you are
| proposing. I am willing to bet that you are wrong.
| aris0 wrote:
| Has anyone gotten this to run on MLX yet?
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