Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Time Series Analysis with AI equips you to model data where order and time matter — demand, sensors, markets, climate. You will start with the fundamentals of stationarity, autocorrelation, seasonality and decomposition, then build classical forecasters such as ARIMA and exponential smoothing. From there you move to machine-learning and deep approaches — feature-based models, Prophet, and LSTM/temporal networks — and learn to validate forecasts honestly with proper backtesting. The emphasis throughout is choosing the right method for the signal in front of you and quantifying uncertainty. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course teaches time-series analysis and forecasting — from ARIMA and decomposition to LSTM and modern deep models — on real temporal data.
📋 Course Objectives
1. Analyse stationarity, seasonality and autocorrelation.
2. Build ARIMA and exponential-smoothing forecasters.
3. Apply Prophet and feature-based ML to forecasting.
4. Model sequences with LSTM and temporal networks.
5. Backtest forecasts and quantify uncertainty.
👥 Who Should Enroll?
• Analysts and data scientists forecasting demand or risk
• Engineers working with sensor and IoT time series
• Researchers in finance, climate or operations
• Students specialising in temporal data
🚀 Key Learning Outcomes
• The ability to build and validate a forecasting model.
• A forecasting project on real temporal data.
• Skills to quantify and communicate forecast uncertainty.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Time Series Analysis Foundations
Develop a comprehensive understanding of artificial intelligence and machine learning fundamentals • Analyze mathematical concepts underlying time series analysis, including probability, statistics, and linear algebra • Design a basic time series analysis pipeline using Python and relevant libraries
Data Engineering, Preprocessing, and Feature Pipelines
Configure data ingestion pipelines using Apache Beam and Google Cloud Dataflow • Implement data preprocessing techniques, including handling missing values and data normalization • Evaluate the effectiveness of various feature engineering methods for time series data
Model Architecture, Algorithm Design, and Time Series Analysis Methods
Design and implement recurrent neural networks (RNNs) and long short-term memory (LSTM) networks for time series forecasting • Analyze the performance of different model architectures, including autoregressive integrated moving average (ARIMA) and exponential smoothing (ES) • Develop a custom model using TensorFlow and Keras for time series analysis
Training, Hyperparameter Optimization, and Evaluation
Train and evaluate time series models using walk-forward optimization and backtesting • Implement hyperparameter tuning using grid search, random search, and Bayesian optimization • Evaluate the performance of time series models using metrics such as mean absolute error (MAE) and mean squared error (MSE)
Deployment, MLOps, and Production Workflows
Deploy time series models using Docker and Kubernetes • Configure model serving pipelines using TensorFlow Serving and AWS SageMaker • Develop a production-ready workflow for time series analysis using Apache Airflow
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze the ethical implications of time series analysis and AI decision-making • Implement bias mitigation techniques, including data preprocessing and model regularization • Develop a framework for responsible AI practices in time series analysis
Industry Integration, Business Applications, and Case Studies
Evaluate the applications of time series analysis in various industries, including finance and healthcare • Develop a business case for implementing time series analysis in a real-world setting • Analyze case studies of successful time series analysis implementations
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Apache Beam |
| Covered Tool / Platform | Google Cloud Dataflow |
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