Study portal • Time Series
Time Series Analysis & Forecasting
Master statistical time series modeling — covering trends, seasonality, stationarity, smoothing techniques, ARIMA/SARIMA models, regression, and practical Python forecasting for BCA & Data Science students.
78
Topics & Labs
47
Core Notes
31
Python Labs
100%
Free Access
Academic Curator: Mr. Vivek Saxena
Educator & AI Research Scholar • Full-Stack Data Professional • Syllabus Aligned
Data & Analytics
Python & Stats
BCA & Data Science
Module 1
Introduction to Time Series
- Introduction to Time Series
- Meaning and Definition of Time Series
- Components of Time Series
- Types of Time Series
- P1.01 Chronological Time Series Data
- P1.02 Classify Types of Data
- P1.03 Identify Internal Structures
- P1.04 Visualize a Basic Time Series
- P1.05 Nature of a Time Series Dataset
- P1.06 Uses of Forecasting
- P1.07 Simple Forecasting Workflow
- P1.08 Prepare Historical Data
- P1.09 Data Sources and Resources
- P1.10 Mini End-to-End Forecast
Module 2
Time Series Analysis and Components
- Trend
- Seasonal Variation
- Cyclical Variation
- Irregular Variation
- P2.01 Graphical Display of a Series
- P2.02 Time Series Plot with EDA
- P2.03 Moving Average Smoothing
- P2.04 Original vs Smoothed Series
- P2.05 Numerical Description
- P2.06 Central Tendency and Dispersion
- P2.07 Logarithmic Transformation
- P2.08 Unusual Observations
- P2.09 General Forecasting Workflow
- P2.10 Evaluate Forecast Performance
Module 3
Statistics Background for Forecasting
- Graphical Displays
- Time Series Plots
- Plotting Smoothed Data
- Numerical Description of Time Series Data
- Use of Data Transformations and Adjustments
- General Approach to Time Series Modelling and Forecasting
- Evaluating and Monitoring Forecasting Model Performance
- P3.01 Calculate and Plot ACF
- P3.02 Interpret the ACF
- P3.03 Calculate and Plot PACF
- P3.04 Interpret the PACF
- P3.05 Fit a Simple AR Model
- P3.06 Fit a Moving-Average Model
- P3.07 Fit an ARMA Model
- P3.08 Fit an ARIMA Model
- P3.09 Forecast Using ARIMA
- P3.10 Seasonal SARIMA Forecasting
Module 4
Introduction to Autoregressive Models and Forecasting
- Autocorrelation and Partial Autocorrelation
- Autoregressive Moving Average (ARMA) Models
- Autoregressive Integrated Moving Average (ARIMA) Models
- Forecasting using ARIMA
- Seasonal Data
- Seasonal ARIMA Models
- Forecasting using Seasonal ARIMA Models
- P4.01 Linear Regression with Time
- P4.02 Least Squares Estimation
- P4.03 Statistical Inference
- P4.04 Predict New Observations
- P4.05 Residual Adequacy Checks
- P4.06 Variable Selection
- P4.07 Weighted Least Squares
- P4.08 GLS for Time Series Data
Module 5
Time Series Regression Model
- Introduction to Time Series Regression
- Least Squares Estimation in Linear Regression Models
- Statistical Inference in Linear Regression
- Prediction of New Observations
- Model Adequacy Checking
- Variable Selection Methods in Regression
- Generalized Least Squares
- Weighted Least Squares
- Regression Models for General Time Series Data
- P5.01 Multivariate Time Series Idea
- P5.02 Explore Related Series
- P5.03 Multivariate Stationary Process
- P5.04 Simple Multivariate Forecast
- P5.05 Compare Multi-Variable Forecasts
- P5.06 Bayesian Forecasting Idea
Module 6