Migas-1.5 — Text-Conditioned Time Series Forecasting

Migas-1.5 is a multimodal late-fusion model that combines a frozen univariate time series forecaster (Chronos-2) with LLM-generated text context to produce forecasts that respond to narrative signals — particularly for financial and economic time series.

Rewrite the narrative, watch the forecast shift.

Architecture

Migas-1.5 uses gated attention fusion:

1. Univariate backbone: A frozen Chronos-2 forecaster produces a base forecast from historical values
2. Context summarization: An LLM generates structured summaries with two sections — FACTUAL SUMMARY: (what happened) and PREDICTIVE SIGNALS: (forward-looking outlook, relative terms only)
3. Text embedding: Summaries are encoded via a text embedder (FinBERT 768d, Qwen3-Embedding-4B 2560d, or Qwen3-Embedding-8B 4096d) and projected to a shared 512d space
4. Gated attention fusion: The time series forecast embedding attends to factual and predictive text embeddings via 4-head cross-attention, modulated by a learned sigmoid gate
5. Convex combination: A learned per-point blending of the univariate base forecast and the fused forecast
6. Forecast head: The fused representation is decoded to the final prediction

Key components

Component	Details
d_model	512
Fusion	4-head cross-attention with sigmoid gating
Text projector	2-layer MLP (Linear -> GELU -> LayerNorm -> Linear -> LayerNorm)
TS embedder	ResidualBlock (Linear -> SiLU + skip connection)
Convex combination	Learned per-point blending of univariate + fused forecasts
Modality dropout	Randomly zeros text embeddings during training (default 0.2)

Model flow

History -> [Chronos-2 (frozen)] -> base forecast
                                        |
                              [TS Embedder] -> h_ts (512d)

Text annotations -> [LLM Summarizer] -> FACTUAL SUMMARY + PREDICTIVE SIGNALS
                                        |
                              [Text Embedder + Projector] -> h_fact, h_pred (512d)

                        [Gated Cross-Attention Fusion]
                              h_ts attends to [h_fact, h_pred]
                                        |
                              [Convex Combination with base forecast]
                                        |
                              [Forecast Head] -> final prediction

Usage

Basic inference with a pre-computed summary

import pandas as pd
from migaseval import MigasPipeline

# Load model from this repo
pipeline = MigasPipeline.from_pretrained(
    "Synthefy/migas-1.5",
    device="cuda",           # or "cpu"
    text_embedder="finbert",  # must match training config
)

# Load your data (columns: t, y_t, text)
df = pd.read_csv("your_data.csv")

summary = """FACTUAL SUMMARY:
Oil prices declined steadily over the past month amid weakening demand signals...

PREDICTIVE SIGNALS:
OPEC production cuts expected to tighten supply, likely supporting prices in the near term..."""

forecast = pipeline.predict_from_dataframe(df, pred_len=16, summaries=summary)

Auto-generate summaries with an LLM

Requires ANTHROPIC_API_KEY (recommended) or OPENAI_API_KEY.

from migaseval.summary_utils import generate_summary

summaries = generate_summary(
    series_name="Crude Oil WTI",
    series=df,
    pred_len=16,
    llm_provider="anthropic",
    n_summaries=9,  # ensemble for stability
)

forecast = pipeline.predict_from_dataframe(df, pred_len=16, summaries=summaries)

Live summarization with a local vLLM server

If you have per-timestep text annotations in your data and want the model to summarize them on-the-fly (instead of passing pre-computed summaries), Migas-1.5 can call a local vLLM server to generate the FACTUAL SUMMARY + PREDICTIVE SIGNALS at inference time.

Start a vLLM server:

bash start_vllm.sh
# or manually:
# vllm serve openai/gpt-oss-120b --port 8004

Configure via environment variables (defaults shown):

export VLLM_BASE_URL="http://localhost:8004/v1"
export VLLM_MODEL="openai/gpt-oss-120b"

Then call predict without summaries — the model will use the text column to generate summaries automatically:

# df must have a 'text' column with per-timestep annotations
forecast = pipeline.predict_from_dataframe(df, pred_len=16)  # no summaries arg

Ensemble mode

Pass multiple summaries to predict_from_dataframe to get a median-aggregated ensemble forecast — reduces variance from stochastic summary generation:

forecast = pipeline.predict_from_dataframe(df, pred_len=16, summaries=summaries)  # list of str

Counterfactual analysis

Rewrite the PREDICTIVE SIGNALS: section to explore "what if" scenarios:

from migaseval.summary_utils import extract_factual

factual = extract_factual(summary)  # keep the facts
bearish = f"{factual}\n\nPREDICTIVE SIGNALS:\nDemand destruction accelerating, prices likely to fall further..."
bullish = f"{factual}\n\nPREDICTIVE SIGNALS:\nSupply cuts expected to tighten market, prices likely to recover..."

forecast_bear = pipeline.predict_from_dataframe(df, pred_len=16, summaries=bearish)
forecast_bull = pipeline.predict_from_dataframe(df, pred_len=16, summaries=bullish)

Data format

Column	Type	Description
t	str (YYYY-MM-DD)	Date
y_t	float	Observed value
text	str	Per-timestep text annotation (used for live LLM summarization; not needed if passing pre-computed summaries)
split	str (optional)	"context" or "ground_truth"

Dependencies

• torch
• chronos-forecasting (Amazon Chronos-2)
• sentence-transformers >= 5.1.1
• huggingface-hub
• anthropic or openai (for summary generation)

Limitations

• Requires an LLM (Anthropic Claude or OpenAI) for automatic summary generation, or pre-computed summaries
• Chronos-2 must be downloadable at inference time (auto-fetched from HuggingFace Hub)
• Text embedder model must match the training configuration (default: FinBERT)
• Primarily evaluated on financial/economic time series
• Prediction horizon fixed at 16 steps (matching training)

Evaluation

Evaluated on held-out test splits (post 2022-12-31) across:

• FNSPID-100 (stock price indices)
• Trading Economics (macroeconomic indicators)

Metrics: MAE, MSE, RMSE, directional accuracy.

Links

• Code & notebooks: Synthefy/synthefy-migas
• Dataset: Synthefy/multimodal_datasets