Time Series Analysis with AI
Master Time Series Analysis with AI: Predict, Detect, and Optimize Trends
Skills you will gain:
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.
What you will learn?
- 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
Intended For :
Data scientists, AI engineers, financial analysts, and researchers working with time-dependent data.
Career Supporting Skills