Coding Guide to Build Aventic Avenic Aventic Aventic Application for Application for Delivery and Configuration
In this lesson, we create an advanced Age AI system that deals with independent communication with time to predict Arrows The Library is compiled with a huggiight model that is not cold to think. We design the agent that we work on a view-action-action – when analyzing patterns in the data, and select the appropriate forecast model, issuing appropriate predictions, eventually explain results. During this Pipeline, we hear how the Agentic AI can bring together mathematical models and the environmental consultation to make accurate predictions and converting. Look Full codes here.
!pip install darts transformers pandas matplotlib numpy -q
import pandas as pd
import numpy as np
from darts import TimeSeries
from darts.models import ExponentialSmoothing, NaiveSeasonal, LinearRegressionModel
from darts.metrics import mape, rmse
from transformers import pipeline
import matplotlib.pyplot as plt
from datetime import datetime, timedeltaWe start by installing and importing the key libraries, including DARDs for Forecasting Series, consultation, and packages support as pandas, Incenda, and matplotlib. With these tools in the area, we laid the foundation for building and using our Independent predications agent. Look Full codes here.
class TimeSeriesAgent:
"""Autonomous agent for time series analysis and forecasting"""
def __init__(self):
print("🤖 Initializing Agent Brain...")
self.llm = pipeline("text-generation", model="distilgpt2", max_length=150,
do_sample=True, temperature=0.7)
self.models = {
'exponential_smoothing': ExponentialSmoothing(),
'naive_seasonal': NaiveSeasonal(K=12),
'linear_regression': LinearRegressionModel(lags=12)
}
self.selected_model = None
self.forecast = None
def perceive(self, data):
"""Agent perceives and analyzes the time series data"""
print("n👁️ PERCEPTION PHASE")
self.ts = TimeSeries.from_dataframe(data, 'date', 'value', freq='M')
trend = "increasing" if data['value'].iloc[-1] > data['value'].iloc[0] else "decreasing"
volatility = data['value'].std() / data['value'].mean()
seasonality = self._detect_seasonality(data['value'])
analysis = {
'length': len(data),
'trend': trend,
'volatility': f"{volatility:.2f}",
'has_seasonality': seasonality,
'mean': f"{data['value'].mean():.2f}",
'range': f"{data['value'].min():.2f} to {data['value'].max():.2f}"
}
print(f"📊 Data Points: {analysis['length']}")
print(f"📈 Trend: {analysis['trend'].upper()}")
print(f"🎲 Volatility: {analysis['volatility']}")
print(f"🔄 Seasonality: {'Detected' if seasonality else 'Not detected'}")
return analysis
def _detect_seasonality(self, series, threshold=0.3):
"""Simple seasonality detection"""
if len(series) < 24:
return False
acf = np.correlate(series - series.mean(), series - series.mean(), mode="full")
acf = acf[len(acf)//2:]
acf /= acf[0]
return np.max(acf[12:24]) > threshold if len(acf) > 24 else False
def reason(self, analysis):
"""Agent reasons about which model to use"""
print("n🧠 REASONING PHASE")
prompt = f"Time series analysis: {analysis['length']} data points, {analysis['trend']} trend, "
f"volatility {analysis['volatility']}, seasonality: {analysis['has_seasonality']}. "
thought = self.llm(prompt, max_length=100, num_return_sequences=1)[0]['generated_text']
print(f"💭 Agent Thinking: {thought[:150]}...")
if analysis['has_seasonality']:
self.selected_model="naive_seasonal"
reason = "Seasonality detected - using Naive Seasonal model"
elif float(analysis['volatility']) > 0.3:
self.selected_model="exponential_smoothing"
reason = "High volatility - using Exponential Smoothing"
else:
self.selected_model="linear_regression"
reason = "Stable trend - using Linear Regression"
print(f"✅ Decision: {reason}")
return self.selected_model
def act(self, horizon=12):
"""Agent takes action: trains model and generates forecast"""
print("n⚡ ACTION PHASE")
train, val = self.ts[:-12], self.ts[-12:]
model = self.models[self.selected_model]
print(f"🎯 Training {self.selected_model}...")
model.fit(train)
self.forecast = model.predict(horizon)
if len(val) > 0:
val_pred = model.predict(len(val))
accuracy = 100 - mape(val, val_pred)
print(f"📊 Validation Accuracy: {accuracy:.2f}%")
print(f"🔮 Generated {horizon}-step forecast")
return self.forecast
def explain(self):
"""Agent explains its predictions"""
print("n💬 EXPLANATION PHASE")
forecast_values = self.forecast.values().flatten()
hist_values = self.ts.values().flatten()
change = ((forecast_values[-1] - hist_values[-1]) / hist_values[-1]) * 100
direction = "increase" if change > 0 else "decrease"
explanation = f"Based on my analysis using {self.selected_model}, "
f"I predict a {abs(change):.1f}% {direction} in the next period. "
f"Forecast range: {forecast_values.min():.2f} to {forecast_values.max():.2f}. "
f"Historical mean was {hist_values.mean():.2f}."
print(f"📝 {explanation}")
prompt = f"Forecast summary: {explanation} Explain implications:"
summary = self.llm(prompt, max_length=120)[0]['generated_text']
print(f"n🤖 Agent Summary: {summary[:200]}...")
return explanation
def visualize(self):
"""Agent creates visualization of its work"""
print("n📊 Generating visualization...")
plt.figure(figsize=(14, 6))
self.ts.plot(label="Historical Data", lw=2)
self.forecast.plot(label=f'Forecast ({self.selected_model})',
lw=2, linestyle="--")
plt.title('🤖 Agentic AI Time Series Forecast', fontsize=16, fontweight="bold")
plt.xlabel('Date', fontsize=12)
plt.ylabel('Value', fontsize=12)
plt.legend(loc="best", fontsize=11)
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()It describes the timeriagont thinking about a huggiight model and works with a small portfolio of DARDS models. We see patterns (habit, flexibility, season), the reason for choosing the best model, and train, forecast, then confirm. Finally, we explain predicting language and visualize climate-predictions. Look Full codes here.
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def create_sample_data():
"""Generate sample time series data"""
dates = pd.date_range(start="2020-01-01", periods=48, freq='M')
trend = np.linspace(100, 150, 48)
seasonality = 10 * np.sin(np.linspace(0, 4*np.pi, 48))
noise = np.random.normal(0, 3, 48)
values = trend + seasonality + noise
return pd.DataFrame({'date': dates, 'value': values})We build a job to help create a_aMample_Data () that produces a series of time series of being made in a clear state, sin soality, random noise. This allows us to imitate the logical data of monthly from 2020 to 2023 for testing and showing the overall agency of the agent. Look Full codes here.
def main():
"""Main execution: Agent autonomously handles forecasting task"""
print("="*70)
print("🚀 AGENTIC AI TIME SERIES FORECASTING SYSTEM")
print("="*70)
print("n📥 Loading data...")
data = create_sample_data()
print(f"Loaded {len(data)} data points from 2020-01 to 2023-12")
agent = TimeSeriesAgent()
analysis = agent.perceive(data)
agent.reason(analysis)
agent.act(horizon=12)
agent.explain()
agent.visualize()
print("n" + "="*70)
print("✅ AGENT COMPLETED FORECASTING TASK SUCCESSFULLY")
print("="*70)
if __name__ == "__main__":
main()It describes the main function that uses the full pipe of Agentic Ai AiPeline. We upload data series, allow TimeriesAsagent to see patterns, the reason for choosing the best model, do with training and prediction, to describe the results, and eventually see results. This ends the coming, indicative insight, and the action cycle.
In conclusion, we see how an independent agent can evaluate the details of the period, the reason regarding model, produce its predictions in nature. By combining arrows with Huggingface, we create an integrated framework but that is only unproductive force but also clearly interpreting understanding. We complete the cycle by seeing it, emphasizing how Agentic Ai make it accurate and interactible predictions.
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