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Time Series Analysis with AI Course

Original price was: INR ₹4,998.00.Current price is: INR ₹2,499.00.

The Time Series Analysis with AI course is a 3-week program designed to teach you how to apply AI techniques to time series data for forecasting, trend analysis, and decision-making. Learn how to build predictive models that can analyze patterns in data over time.

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Aim

This course aims to provide advanced skills in analyzing and forecasting time series data using AI techniques. Participants will learn how to apply AI-powered tools to model, predict, and optimize time series data in industries like finance, healthcare, and manufacturing.

Program Objectives

  • Master Time Series Analysis: Learn fundamental techniques in time series data analysis.
  • Apply AI for Forecasting: Use AI models like LSTM and RNN for time series forecasting.
  • Anomaly Detection: Understand how to detect anomalies in time series data using AI.
  • Multivariate Time Series: Gain proficiency in handling multivariate time series data and making predictions.
  • Build Real-Time Models: Develop real-time AI forecasting models for diverse industries.

Program Structure

Module 1: Introduction to Time Series Analysis

  • Overview of time series data and its applications.
  • Differences between time series and traditional data.
  • Applications in finance, healthcare, and IoT.

Module 2: Fundamentals of Time Series Data

  • Components of time series data: Trend, seasonality, and noise.
  • Types of time series: Univariate and multivariate.
  • Data preprocessing: Handling missing data, smoothing, and transformations.

Module 3: Exploratory Data Analysis (EDA) for Time Series

  • Visualization techniques for time series data.
  • Statistical methods for EDA, including stationarity and differencing.

Module 4: Classical Time Series Models

  • Moving average (MA), autoregressive (AR), and ARIMA models.
  • Seasonal Decomposition of Time Series (STL) and Exponential Smoothing (Holt-Winters).

Module 5: Machine Learning for Time Series Forecasting

  • Feature engineering for time series data.
  • Regression techniques for time series forecasting.
  • Using models like Random Forests and Gradient Boosting.

Module 6: Deep Learning for Time Series

  • Applying Recurrent Neural Networks (RNNs) for sequential data.
  • Using LSTM and GRU networks for long-term dependencies.
  • CNNs for time series forecasting.

Module 7: Advanced Time Series Techniques with AI

  • Temporal Convolutional Networks (TCN).
  • Transformers for time series data.
  • Sequence-to-sequence models for prediction.

Module 8: Time Series Anomaly Detection with AI

  • Introduction to anomaly detection in time series.
  • AI techniques like Autoencoders and Isolation Forests for detecting anomalies.
  • Applications in fraud detection and network intrusion detection.

Module 9: Multivariate Time Series Analysis

  • Working with multiple time series variables.
  • Vector Autoregression (VAR) and AI-based methods for multivariate prediction.

Module 10: Probabilistic Forecasting and Uncertainty

  • Bayesian methods for forecasting.
  • Quantifying uncertainty in AI models.
  • Applications in demand forecasting and weather prediction.

Module 11: Real-Time Time Series Analysis

  • Real-time data streams and online learning for time series.
  • AI for real-time forecasting and decision-making.
  • Use cases: Real-time stock market analysis, IoT monitoring.

Participant Eligibility

  • Data Scientists: Professionals working with time-dependent data.
  • AI Engineers: Individuals applying AI techniques for forecasting and predictions.
  • Financial Analysts: Analysts dealing with market trends and stock predictions.
  • Researchers: Individuals in need of time series expertise for data analysis.

Program Outcomes

  • AI-Based Time Series Modeling: Proficiency in building and deploying AI-driven time series models.
  • Expertise in LSTM and RNNs: Learn to use LSTM, RNNs, and ARIMA models for sequential data prediction.
  • Anomaly Detection Skills: Gain experience in real-time anomaly detection and predictive maintenance.
  • Practical AI Solutions: Hands-on experience in building AI-powered forecasting models for various industries.

Program Deliverables

  • Access to e-LMS: Complete access to course materials online.
  • Real-Time Project: Engage in practical, real-time projects using AI for time series analysis.
  • Project Guidance: Expert mentorship for project development and implementation.
  • Research Publication Opportunity: Support for publishing research findings on time series AI.
  • Final Examination: Certification awarded based on mid-term assignments and final project submission.
  • e-Certification: Digital certificate upon successful completion.

Future Career Prospects

  • Time Series Data Scientist: Specialize in analyzing and predicting time series data.
  • AI Forecasting Specialist: Focus on using AI techniques to forecast trends.
  • Predictive Analytics Engineer: Build predictive models for business optimization.
  • Financial Forecasting Analyst: Apply AI to financial markets and stock price predictions.
  • Supply Chain Data Scientist: Use AI to optimize logistics and inventory forecasting.
  • AI Operations Engineer: Focus on AI-powered operational forecasting in industries like IoT and manufacturing.

Job Opportunities

  • Companies focused on demand forecasting, predictive maintenance, and real-time analytics.
  • Financial institutions requiring AI-based market prediction tools.
  • Enterprises implementing AI-powered anomaly detection in IoT environments.
MODE

Online/ e-LMS
TYPE

Self Paced
LEVEL

Moderate
DURATION

3 Weeks

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