Forecasting

NoriTSForecaster converts a time series into tabular features and predicts a requested number of future steps. It returns a pandas DataFrame containing the median forecast and any requested quantiles. Forecasting runs locally and requires the timeseries extra.

Quickstart

Install the forecasting dependencies

Follow Installation to install the timeseries extra for synthefy-nori.

Create a history DataFrame

Provide timestamp and target columns, ordered from oldest to newest.

import numpy as np
import pandas as pd
from synthefy_nori.nori_ts import NoriTSForecaster

periods = 500
history = pd.DataFrame({
    "timestamp": pd.date_range("2024-01-01", periods=periods, freq="h"),
    "target": 10 + np.sin(2 * np.pi * np.arange(periods) / 24),
})

Forecast the horizon

predict_df returns one row per future timestamp.

forecaster = NoriTSForecaster(
    model="nori-30m",
    quantiles=[0.1, 0.5, 0.9],
)
forecast = forecaster.predict_df(history, prediction_length=48)

print(forecast.columns.tolist())
print(forecast.index.names)
print(forecast.shape)

['target', '0.1', '0.5', '0.9']
['item_id', 'timestamp']
(48, 4)

target is the median forecast. The other columns contain the requested quantiles.

How it works

Tabular conversion

For each series, the forecaster creates rows indexed by (item_id, timestamp). Historical rows contain the observed target, and future rows contain generated time features. Nori fits on the historical rows and predicts the future rows.

The time features

The default features include a running index, calendar sine and cosine values, and seasonal periods detected from the history. You can replace them with custom feature generators through features=.

Quantile output

Set quantiles= to choose the returned quantile columns. The target column contains the median forecast.

Configuration

NoriTSForecaster parameters

Parameter	Default	Description
`quantiles`	`[0.1 … 0.9]`	Forecast quantile levels; the median is the point forecast.
`context_length`	`4096`	Cap on history rows per series (last-N kept).
`device`	`None`	Torch device (e.g. `"cuda:0"`); `None` auto-selects.
`features`	`None`	Feature generators; defaults to the standard set.
`model`	`"nori-6m"`	Nori checkpoint variant; use `"nori-6m"` or `"nori-30m"`.
`model_path`	`None`	Explicit checkpoint path; overrides `model`. `None` resolves the named model from Hugging Face.

predict_df(context_df, prediction_length)

context_df needs a timestamp and a target column (an optional item_id for multi-series frames). Returns a DataFrame indexed by (item_id, timestamp) over the horizon, with a target (median) column and one column per quantile level ("0.1", "0.5", …).

forecast = NoriTSForecaster(
    model="nori-30m",
    quantiles=[0.1, 0.5, 0.9],
).predict_df(
    history,
    prediction_length=48,
)

For frames already in (item_id, timestamp) form, predict(train_tsdf, test_tsdf) is the lower-level entry point.

Next steps

Continue with Text Features, or browse more local Nori workflows in Examples.

Get Started

Guides

Integration

Quickstart

How it works

Configuration

Next steps

​Quickstart

​How it works

​Configuration

​Next steps

Quickstart

How it works

Configuration

Next steps