A pull request was merged after being reviewed for 5 days, 0 hours: mlforecast/[FEAT] Add step size argument to cross validation method

A pull request that waited for review 7 days, 4 hours has been merged: mlforecast/[FEAT] Add step size argument to cross validation method

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A pull request that waited for review 2 days, 17 hours has been merged: neuralforecast/[FEAT] Optimization improvements and new features

A pull request that waited for review 4 days, 3 hours has been merged: mlforecast/[FEAT] Add

new_data

argument to

predict

method (allow transferability)

A pull request with 48 code changes was merged without review: statsforecast/[FEAT] Test recover M3 performance

A pull request that waited for review 2 days, 17 hours has been merged: neuralforecast/[FEAT] Added

valid_loss

possibilities

A pull request that waited for review 3 hours, 27 minutes has been merged: neuralforecast/[FEAT] Zero Inflated and Categorical Distributions (NBinomial, Tweedie, Bernoulli)

A pull request with 35 code changes was merged without review: mlforecast/automate release

A pull request that waited for review 2 hours, 24 minutes has been merged: neuralforecast/[FEAT] Added predict_rolled method

A pull request that waited for review 20 hours, 57 minutes has been merged: mlforecast/[DOCS] Add transfer learning tutorial

A pull request with 48 code changes was merged without review: statsforecast/Update README.md

A pull request with 35 code changes was merged without review: mlforecast/add cross validation fitted values

A pull request with 46 code changes was merged without review: mlforecast/allow id_col in static_features

A pull request that waited for review 1 day, 9 hours has been merged: mlforecast/allow id_col in static_features

Kraft Heinz is HIRING data scientists! We are looking to fill 3 roles by individuals with experience in time-series forecasting! Are you interested? Apply now, currently interviewing for these roles! Candidates must be US located. Headquartered in Chicago, IL but remote US also available. Principal Data Scientist: https://lnkd.in/gxrXbvt4 Senior Manager of Data Science: https://lnkd.in/gPfTWdKD Senior Data Scientist: https://lnkd.in/gCUNy2QG

Hi there @Max (Nixtla) @Cristian (Nixtla). I have a general question about how

mlforecast

,

statsforecast

and

neuralforecast

can interact (or not?). From what I understand,

mlforecast

allows to train and apply any models from sklearn as well as lightgbm/xgboost. Is it possible to also use models from

statsforecast

(e.g. AutoARIMA) or from

neuralforecast

(e.g. NBEATS) ?

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Or am I missing something here ?

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Out of curiosity, have any of you tried some timeseries data augmentation techniques like the block bootstrap. I was thinking of Hyndman's Bagged ETS model. https://otexts.com/fpp2/bootstrap.html#bagged-ets-forecasts That got me thinking about having a bagged bootstrap timeseries loader for every batch that goes into one of your models.

Hi, is it possible to save a trained MLForecast model object and later load it for prediction ?

Google released their novel TiDE forecasting model to Vertex AI: https://cloud.google.com/blog/products/ai-machine-learning/vertex-ai-forecasting

🎙️ Testing, testing… is this thing on?

Greetings. I'm writing my final monograph on Transformers for Financial Time Series and I just found Nixtla's github. It will definitely simplify my work and I decided to use it. I hope I can count on this community to help me.

Nice work @Rodrigo Sodré. Is the portuguese version publicly available? (eu sou brasileiro 🙂 )

I’m new to the Slack community and excited to be here! Could anyone please share their insights or predictions for BTC price over the following timeframes? 4 hours 1 day 1 week Looking forward to your thoughts. Thanks in advance!

Hi all! Would this be an appropriate implementation of the Simple Combination of Univariate Model (SCUM) if I have monthly data (all positive) and I want to generate 2 years worth of predictions?

# Monthly dataset:
df = df[['unique_id', 'ds', 'y']]

seasonality = 12 

models = [
    AutoETS(model = 'ZZZ', season_length = seasonality),
    DynamicOptimizedTheta(season_length = seasonality),
    AutoCES(season_length = seasonality),
    AutoARIMA(season_length = seasonality)
]

# Instantiate StatsForecast class
sf = StatsForecast( 
    df = d_new,
    models = models,
    freq = 'MS', 
    n_jobs = -1,
    fallback_model = SeasonalNaive(season_length = seasonality)
)

sf.fit()

d_sf = sf.predict(h=24)
model_cols = [c for c in d_sf.columns if c != 'ds']

d_sf['yhat'] = d_sf[model_cols].clip(0).median(axis=1, numeric_only=True)

hey all! anybody coming to iclr 2025?

Hi! Is there a working link to: "example" under the cross validation Evaluate Results section? https://nixtlaverse.nixtla.io/statsforecast/docs/tutorials/crossvalidation.html#evaluate-results