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Time Series Analysis with AI Course – 3 Weeks
Original price was: USD $78.00.USD $39.00Current price is: USD $39.00.
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.
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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