Online/ e-LMS
Self Paced
Moderate
3 Weeks
About
The program focuses on AI-based approaches such as deep learning, LSTM, ARIMA, and machine learning techniques for analyzing time-dependent data. It covers methods for predicting trends, detecting anomalies, and applying real-time forecasting using AI.
Aim
To provide advanced skills in analyzing and forecasting time series data using AI techniques. This course will guide participants through AI-powered tools to model, predict, and optimize time series data for industries like finance, healthcare, and manufacturing.
Program Objectives
- Learn fundamental techniques in time series analysis.
- Apply AI models like LSTM and RNN for time series forecasting.
- Understand how to detect anomalies using AI.
- Gain proficiency in multivariate time series predictions.
- Build real-time AI forecasting models for various industries.
Program Structure
- Introduction to Time Series Analysis
- Overview of Time Series Data
- Time Series vs. Traditional Data
- Applications of Time Series in Finance, Healthcare, and IoT
- Fundamentals of Time Series Data
- Components of Time Series: Trend, Seasonality, and Noise
- Types of Time Series: Univariate and Multivariate
- Data Preprocessing for Time Series: Handling Missing Data, Smoothing, and Transformation
- Exploratory Data Analysis (EDA) for Time Series
- Visualization Techniques for Time Series Data
- Statistical Methods for Time Series EDA
- Stationarity and Differencing
- Classical Time Series Models
- Moving Average (MA), Autoregressive (AR), and ARIMA Models
- Seasonal Decomposition of Time Series (STL)
- Exponential Smoothing (Holt-Winters)
- Machine Learning for Time Series Forecasting
- Feature Engineering for Time Series
- Regression Techniques for Time Series Forecasting
- Random Forests and Gradient Boosting for Time Series
- Deep Learning for Time Series
- Recurrent Neural Networks (RNNs) for Sequential Data
- Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
- Using CNNs for Time Series Forecasting
- Advanced Time Series Techniques with AI
- Temporal Convolutional Networks (TCN)
- Transformers for Time Series Forecasting
- Sequence-to-Sequence Models
- Time Series Anomaly Detection with AI
- Introduction to Anomaly Detection in Time Series
- AI Techniques for Anomaly Detection (Autoencoders, Isolation Forest)
- Applications: Fraud Detection, Network Intrusion Detection
- Multivariate Time Series Analysis
- Working with Multiple Time Series Variables
- Vector Autoregression (VAR)
- AI Techniques for Multivariate Time Series Prediction
- Probabilistic Forecasting and Uncertainty in Time Series
- Bayesian Methods for Time Series Forecasting
- Quantifying Uncertainty in AI Models
- Applications in Demand Forecasting, Weather Forecasting
- Real-Time Time Series Analysis
- Real-Time Data Streams and Online Learning
- AI for Real-Time Forecasting and Decision Making
- Use Cases: Real-Time Stock Market Analysis, IoT Monitoring
Participant’s Eligibility
Data scientists, AI engineers, financial analysts, and researchers working with time-dependent data.
Program Outcomes
- Proficiency in AI-based time series modeling and forecasting.
- Ability to use LSTM, RNNs, and ARIMA for sequential data.
- Real-time anomaly detection and predictive maintenance skills.
- Practical experience in building AI-powered forecasting solutions.
Fee Structure
Standard Fee: INR 4,998 USD 78
Discounted Fee: INR 2499 USD 39
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
List of CurrenciesBatches
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Key Takeaways
Program Assessment
Certification to this program will be based on the evaluation of following assignment (s)/ examinations:
| Exam | Weightage |
|---|---|
| Mid Term Assignments | 50 % |
| Project Report Submission (Includes Mandatory Paper Publication) | 50 % |
To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Future Career Prospects
- Time Series Data Scientist
- AI Forecasting Specialist
- Predictive Analytics Engineer
- Financial Forecasting Analyst
- Supply Chain Data Scientist
- AI Operations Engineer
Job Opportunities
- Companies focusing 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
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