Prediction & Forecasting

AI, ML, Stats, Prediction, Forecasting, Regression, Time Series

Prediction & Forecasting #

Prediction and forecasting use statistical models to estimate unknown or future values.

In this module, the focus is on correlation, regression, and time series forecasting.

Key takeaway:
Prediction estimates a value using a model.
Forecasting is prediction where the order of time matters.

Correlation
Regression
Time series analysis
Components of time series data
Moving average and weighted moving average
AR model
ARMA model
ARIMA model
SARIMA and SARIMAX
VAR and VARMAX
Simple exponential smoothing

Prediction vs Forecasting ☆ #

Concept	Meaning	Example
Prediction	Estimate an unknown output	Predict house price from area and rooms
Forecasting	Predict future values using time order	Forecast sales for next month

All forecasting is prediction, but not all prediction is forecasting.

Overall Workflow #

flowchart LR
    A[Data] --> B[Explore Pattern]
    B --> C[Choose Model]
    C --> D[Train or Fit]
    D --> E[Validate]
    E --> F[Predict or Forecast]
    F --> G[Interpret Error]

    style A fill:#E1F5FE
    style B fill:#C8E6C9
    style C fill:#FFF9C4
    style D fill:#EDE7F6
    style E fill:#C8E6C9
    style F fill:#E1F5FE
    style G fill:#FFF9C4

Correlation ☆ #

Correlation measures the direction and strength of linear relationship between two variables.

Recurrent Neural Networks

April 19, 2026

AI, Deep Learning

Deep Learning, RNN, Recurrent Neural Networks, Sequence Modelling, BPTT, Encoder Decoder, Teacher Forcing, Time Series

Recurrent Neural Networks #

Recurrent Neural Networks (RNNs) are neural networks designed for sequential data, where the order of inputs matters and the model must use information from earlier time steps to interpret later ones. Unlike a feedforward network, an RNN does not process each input in isolation. It carries a hidden state from one time step to the next, so the network can build a running summary of what it has seen so far.