The Time Series Forecasting with ARIMA and LSTM is a hands-on program designed to help learners master predictive analytics for real-world data. This course covers the complete workflow of time-dependent data — including trend, seasonality, and noise analysis — along with advanced forecasting techniques. Students will learn to build statistical ARIMA models as well as deep learning-based LSTM neural networks to accurately predict future values across domains such as finance, retail, manufacturing, and sensor analytics. Through practical projects and industry datasets, you will gain the skills to transform raw time series data into actionable insights and deploy AI-driven forecasting solutions for business decision-making.
Module 1: Introduction to Time Series Analysis Understanding time series data Components: trend, seasonality, cyclic patterns Stationarity concepts Basic statistical properties.
Module 2: Data Preprocessing & Exploratory Analysis Handling missing values & outliers Resampling & smoothing techniques Normalization, transformations (Log, Box-Cox) Time series decomposition.
Module 3: Classical Forecasting Models – AR, MA, ARMAn Autocorrelation, PACF interpretation Lag features Building AR & MA models Performance evaluation.
Module 4: ARIMA, SARIMA & ARIMAX Models ARIMA modeling workflow AIC, BIC, model diagnostics Seasonal ARIMA (SARIMA) Exogenous variables (ARIMAX) Multi-step forecasting.
Module 5: Feature Engineering for Time Series Rolling windows Lagged variables Date-time features (week, month, holidays) Handling seasonality & trends.
Module 6: Introduction to Deep Learning for Forecasting Understanding sequential data Recurrent Neural Networks basics Vanishing gradient & need for LSTM/GRU Train-test split for time series.
Module 7: LSTM Architecture & Model Building Designing LSTM networks in Keras/TensorFlow Single-step vs multi-step forecasting Stacked LSTM, Bidirectional LSTM Hyperparameter tuning.
Module 8: Advanced Deep Learning Models GRU networks Encoder–Decoder models Convolutional + LSTM hybrid models Attention mechanisms (optional).
Module 9: Model Deployment & Automation Exporting models Building forecasting APIs using Flask/FastAPI Deploying to AWS/GCP/Azure Batch scheduling with Airflow.
Module 10: Real-World Projects & Case Studies Stock price prediction Sales & demand forecasting Power load/energy forecasting Traffic/time-dependent pattern analysis Final project presentation & documentation.
Mobile: 9100348679
Email: coursedivine@gmail.com
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