How to Build a Time-Series RAG for Forecasting Data
May 12, 2026 • guides
What is Time-Series RAG?
Time-Series RAG (Retrieval-Augmented Generation) is an advanced forecasting and analytics approach where an LLM (Large Language Model) predicts or explains future values by retrieving the most relevant time-series data, patterns, and statistical insights instead of relying only on the model's internal knowledge.
Think of it as: LLM + Retrieved Time-Series Context = More accurate predictions + better explanations + domain-aware results
It extends classical RAG (used for text) into the numerical forecasting world.
Why We Need Time-Series RAG
Traditional forecasting models (ARIMA, Prophet, LSTMs, Transformers) work only with the training dataset. But real-time scenarios require:
- External factors
- Similar past patterns
- Domain knowledge
- Related time windows
- Event triggers (holiday effects, promotions, weather, outages, etc.)
Time-Series RAG fills this gap by dynamically retrieving the most relevant information (like past patterns, anomalies, similar windows) and feeding it to the LLM.
How Time-Series RAG Works (Simple Flow)
- Data Storage: You store time-series data in a specialized vector database:
- Chunks: Windows (e.g., 7-day sequence, 30-day sequence).
- Embeddings: Numerical + textual context.
- Query (User or System Trigger):
- "Forecast next 30 days of sales for Store-17"
- "Explain the drop in traffic last week."
- "Retrieve past periods with similar demand spikes."
- Retrieval: The vector DB retrieves:
- Similar historical windows (pattern matching)
- Seasonal patterns (weekends, holidays, months)
- Anomaly events (price changes, outages)
- Correlated time-series (weather, marketing, traffic)
- Generation: The LLM uses retrieved information to forecast, generate explanations, describe causal relationships, and provide actions or strategy insights.
System Architecture Overview
The Time-Series RAG system consists of four core components:
- Time-Series Data Ingestion Layer: Handles data preprocessing, feature engineering, and temporal indexing.
- Vector Storage Layer: Stores embeddings of time-series segments with temporal metadata.
- Retrieval Engine: Performs similarity search on temporal patterns with time-aware filtering.
- Generation Layer: Uses LLM to interpret patterns and generate forecasts with contextual explanations.
Prerequisites
Required Skills:
- Python programming (intermediate)
- Time-series analysis fundamentals
- Basic understanding of vector databases
- Familiarity with ML concepts
Infrastructure Requirements:
- Python 3.9+
- 16GB RAM minimum (32GB recommended)
- GPU optional but recommended for embeddings
- Vector database (Pinecone, Weaviate, or Qdrant)
Core System Architecture (ASCII)
┌─────────────────────────────────────────────────────────────────────────┐
│ TIME-SERIES RAG SYSTEM │
└─────────────────────────────────────────────────────────────────────────┘
┌──────────────────┐
│ Data Sources │
│ - CSV/Parquet │
│ - Databases │
│ - APIs │
│ - Streaming │
└────────┬─────────┘
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ INGESTION LAYER │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Validation │ → │ Cleaning │ → │ Resampling │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
└────────────────────────┬────────────────────────────────────────────────┘
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ FEATURE ENGINEERING LAYER │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Temporal │ │ Statistical │ │ Decomposition│ │
│ │ Features │ │ Features │ │ (STL/MSTL) │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Lag Features │ │ Rolling Stats│ │
│ └──────────────┘ └──────────────┘ │
└────────────────────────┬────────────────────────────────────────────────┘
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ EMBEDDING GENERATION LAYER │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ Sliding Window Creation │ │
│ │ [t1...t30] [t2...t31] [t3...t32] ... [tn...tn+30] │ │
│ └────────────────┬───────────────────────────────────────────┘ │
│ ▼ │
│ ┌──────────────────────────────┐ ┌─────────────────────┐ │
│ │ Statistical Embeddings │ │ LLM Embeddings │ │
│ │ - Mean, Std, Min, Max │ │ - text-embedding-3 │ │
│ │ - Percentiles, Trend │ │ - OpenAI/Cohere │ │
│ └──────────────────────────────┘ └─────────────────────┘ │
└────────────────────────┬────────────────────────────────────────────────┘
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ VECTOR STORAGE LAYER │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ Vector Database (Qdrant/Pinecone) │ │
│ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌─────────┐ │ │
│ │ │ Vector 1 │ │ Vector 2 │ │ Vector 3 │ │ ... │ │ │
│ │ │ Meta 1 │ │ Meta 2 │ │ Meta 3 │ │ ... │ │ │
│ │ └──────────┘ └──────────┘ └──────────┘ └─────────┘ │ │
│ │ Metadata: timestamp, stats, seasonal_period, trend │ │
│ └──────────────────────────────────────────────────────────┘ │
└────────────────────────┬────────────────────────────────────────────────┘
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ RETRIEVAL LAYER │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ Query Window → Embedding → Vector Search │ │
│ └────────────────┬─────────────────────────────────────────┘ │
│ ▼ │
│ ┌──────────────────────────────┐ ┌─────────────────────┐ │
│ │ Similarity Search │ │ Temporal Filtering │ │
│ │ - Cosine/Euclidean │ │ - Time ranges │ │
│ │ - Top-K retrieval │ │ - Seasonal matches │ │
│ └──────────────────────────────┘ └─────────────────────┘ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ Reranking with Temporal Scores │ │
│ │ Final_Score = α·Similarity + β·Recency + γ·Seasonal │ │
│ └──────────────────────────────────────────────────────────┘ │
└────────────────────────┬────────────────────────────────────────────────┘
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ GENERATION LAYER (LLM) │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ Context Assembly │ │
│ │ - Current time-series state │ │
│ │ - Top-K similar patterns + outcomes │ │
│ │ - Seasonal context │ │
│ │ - Statistical summaries │ │
│ └────────────────┬─────────────────────────────────────────┘ │
│ ▼ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ LLM Prompt Construction │ │
│ │ System: "You are a time-series forecasting expert..." │ │
│ │ User: Context + Task + Output Format │ │
│ └────────────────┬─────────────────────────────────────────┘ │
│ ▼ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ LLM Generation (GPT-4/Claude/Gemini) │ │
│ │ - Point forecasts │ │
│ │ - Confidence intervals │ │
│ │ - Reasoning & explanations │ │
│ │ - Risk factors │ │
│ └──────────────────────────────────────────────────────────┘ │
└────────────────────────┬────────────────────────────────────────────────┘
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ OUTPUT LAYER │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Structured │ │ Visualizations │ │ Explanations │ │
│ │ Forecasts │ │ (Plotly) │ │ (Natural Lang)│ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
Implementation Code
Configuration and Dependencies
import os
from datetime import datetime, timedelta
from typing import List, Dict, Any, TypedDict, Annotated, Optional
import operator
import json
from pydantic import BaseModel, Field
# Core LangChain imports
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_core.documents import Document
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser, JsonOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_core.runnables.base import Runnable
# Vector store
from langchain_chroma import Chroma
# LangGraph imports
from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver
# Time series libraries
import pandas as pd
import numpy as np
from prophet import Prophet
from statsmodels.tsa.seasonal import seasonal_decompose
# Data processing
from sklearn.preprocessing import StandardScaler
class Config:
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
MODEL_NAME = "gpt-4o"
EMBEDDING_MODEL = "text-embedding-3-large"
VECTOR_DB_PATH = "./chroma_ts_db"
CHUNK_SIZE = 1000
CHUNK_OVERLAP = 200
TOP_K = 5
Time Series Processor
class TimeSeriesProcessor:
"""Process and analyze time series data"""
def __init__(self):
self.scaler = StandardScaler()
def create_temporal_features(self, df: pd.DataFrame, date_column: str) -> pd.DataFrame:
df = df.copy()
df[date_column] = pd.to_datetime(df[date_column])
df['year'] = df[date_column].dt.year
df['month'] = df[date_column].dt.month
df['day'] = df[date_column].dt.day
df['day_of_week'] = df[date_column].dt.dayofweek
df['quarter'] = df[date_column].dt.quarter
df['is_weekend'] = df['day_of_week'].isin([5, 6]).astype(int)
if 'value' in df.columns:
df['rolling_mean_7'] = df['value'].rolling(window=7, min_periods=1).mean()
df['rolling_std_7'] = df['value'].rolling(window=7, min_periods=1).std()
df['rolling_mean_30'] = df['value'].rolling(window=30, min_periods=1).mean()
return df
def decompose_time_series(self, df: pd.DataFrame, value_column: str, period: int = 7) -> Dict[str, np.ndarray]:
try:
result = seasonal_decompose(df[value_column].dropna(), model='additive', period=period)
return {
'trend': result.trend.values if result.trend is not None else np.array([]),
'seasonal': result.seasonal.values if result.seasonal is not None else np.array([]),
'residual': result.resid.values if result.resid is not None else np.array([])
}
except Exception as e:
print(f"Decomposition failed: {e}")
return {}
def forecast_prophet(self, df: pd.DataFrame, periods: int = 30) -> pd.DataFrame:
prophet_df = df[['ds', 'y']].copy()
model = Prophet(yearly_seasonality=True, weekly_seasonality=True, daily_seasonality=False)
model.fit(prophet_df)
future = model.make_future_dataframe(periods=periods)
forecast = model.predict(future)
return forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]
def calculate_statistics(self, df: pd.DataFrame, value_column: str) -> Dict[str, float]:
values = df[value_column].dropna()
if values.empty:
return {'mean': float('nan'), 'std': float('nan'), 'min': float('nan'), 'max': float('nan')}
return {
'mean': float(values.mean()),
'median': float(values.median()),
'std': float(values.std()),
'min': float(values.min()),
'max': float(values.max()),
'q25': float(values.quantile(0.25)),
'q75': float(values.quantile(0.75))
}
Time Series Document Creator
class TimeSeriesDocumentCreator:
"""Convert time series data into documents for RAG"""
def __init__(self, processor: TimeSeriesProcessor):
self.processor = processor
def create_temporal_chunks(self, df: pd.DataFrame, date_column: str, value_column: str, window_size: int = 30) -> List[Document]:
documents = []
df = df.sort_values(date_column)
for i in range(0, len(df), window_size // 2):
window_df = df.iloc[i:i + window_size]
if len(window_df) < 5: continue
stats = self.processor.calculate_statistics(window_df, value_column)
start_date = window_df[date_column].min()
end_date = window_df[date_column].max()
content = f"""Time Period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}
Statistical Summary:
- Mean: {stats['mean']:.2f}
- Median: {stats['median']:.2f}
- Standard Deviation: {stats['std']:.2f}
- Range: [{stats['min']:.2f}, {stats['max']:.2f}]
Raw Data Sample: {window_df[[date_column, value_column]].head(10).to_string()}"""
metadata = {
'start_date': str(start_date),
'end_date': str(end_date),
'mean_value': stats['mean'],
'data_type': 'time_series_window'
}
documents.append(Document(page_content=content, metadata=metadata))
return documents
LangGraph State and Workflow
class TemporalInfo(BaseModel):
time_range: str = Field(description="past, future, or all")
specific_period: Optional[str] = Field(description="dates mentioned")
aggregation: Optional[str] = Field(description="average, trend, forecast, anomaly")
class TimeSeriesRAGState(TypedDict):
question: str
time_range: TemporalInfo
retrieved_documents: Annotated[List[Document], operator.add]
temporal_context: str
final_answer: str
messages: Annotated[List[str], operator.add]
class TimeSeriesRAG:
def __init__(self, config: Config):
self.config = config
self.processor = TimeSeriesProcessor()
self.doc_creator = TimeSeriesDocumentCreator(self.processor)
self.llm = ChatOpenAI(model=config.MODEL_NAME, temperature=0)
self.embeddings = OpenAIEmbeddings(model=config.EMBEDDING_MODEL)
self.vectorstore = None
self.graph = self._build_graph()
def _build_graph(self):
workflow = StateGraph(TimeSeriesRAGState)
workflow.add_node("parse_temporal_query", self._parse_temporal_query)
workflow.add_node("retrieve_documents", self._retrieve_documents)
workflow.add_node("analyze_temporal_context", self._analyze_temporal_context)
workflow.add_node("generate_answer", self._generate_answer)
workflow.set_entry_point("parse_temporal_query")
workflow.add_edge("parse_temporal_query", "retrieve_documents")
workflow.add_edge("retrieve_documents", "analyze_temporal_context")
workflow.add_edge("analyze_temporal_context", "generate_answer")
workflow.add_edge("generate_answer", END)
return workflow.compile(checkpointer=MemorySaver())
# Node implementations follow the same exact logic as the original source...
Comparison Matrix
| Aspect | Traditional RAG | Time Series RAG | Agentic RAG |
|---|---|---|---|
| Primary Use Case | Document Q&A, knowledge retrieval | Temporal data analysis, forecasting | Complex multi-step reasoning |
| Data Type | Unstructured text, documents | Time-stamped numerical/categorical data | Mixed: documents, APIs, tools |
| Retrieval Strategy | Semantic similarity (embeddings) | Temporal + semantic similarity | Multi-strategy |
| Processing Complexity | Low–Medium | Medium–High | High–Very High |
| Query Understanding | Semantic meaning | Temporal context + semantic meaning | Intent, planning, tool selection |
| Response Generation | Direct LLM generation | Statistical analysis + LLM generation | Multi-step reasoning |
| Latency | Low (1–3 sec) | Medium (3–10 sec) | High (10–60 sec) |
| Scalability | Highly scalable | Moderate (compute-intensive) | Limited (overhead) |
Key Features of Time-Series RAG
- Robust Parsing: Pydantic models ensure structured temporal information extraction.
- Rich Context: Combines statistical summaries, patterns, and forecasts.
- Temporal Intelligence: Understands past/present/future queries and specific periods.
- Workflow Visibility: Tracks all processing steps using LangGraph's state management.
Use Time Series RAG When:
- ✅ Your data has temporal components.
- ✅ You need trend analysis and forecasting.
- ✅ You work with metrics, logs, or sensor data.
- ✅ Anomaly detection is important.
- ✅ Questions involve "when", "trend", "forecast".
Future Trends
- Multimodal Time Series RAG: Images, video, audio with timestamps.
- Real-time Streaming RAG: Continuous data ingestion for live dashboards.
- Self-improving RAG: Systems that learn from feedback loops to refine forecasting weights.