Python: Constructing Time Series Sequence Samples Dataset

This post construct the multivariate time series data into sequence samples dataset for RNNs, LSTMs, CNNs, and similar models in Keras or Tensorflow.

Python: Independent Component Analysis (ICA) versus PCA

This post demonstrates the usage of Independent Component Analysis (ICA) in Python, a technique akin to PCA but focused on independent factors (source signals).

Python: t-SNE dimensional reduction technique

This post shows how to use the t-SNE (t-Distributed Stochastic Neighbor Embedding) in Python, which is a non-linear probabilistic technique for dimensionality reduction.

TensorFlow: Weighted Average Layer with Trainable Weights

This post implements TensorFlow code that estimates the weights used to construct an output composite time series from three input time series.

TensorFlow: Weighted Average Layer with Fixed Weights

This post demonstrates the process of implementing a TensorFlow code to calculate the weighted average of the previous layer's output. This simple exercise serves as a practical guide to creating a custom layer in TensorFlow.

TensorFlow: Variational Autoencoder (VAE) for MNIST Digits

This post demonstrates the implementation of Tensorflow code for Variational Autoencoder (VAE) using a well-established example with MNIST digit data.

Python: Solving ODEs with Deep Learning in TensorFlow

This post explains how to implement TensorFlow code to solve ODE problems. This approach is known as 'Physics-Informed Neural Networks' (PINNs) or 'Neural ODEs.'

PyTorch : A Simple MLP model for Univariate Time Series Forecasting

This post explains how to implement a simple MLP model for a univariate time series forecasting using PyTorch. The estimation of the PyTorch model is slightly different from the Keras model, so it's worth focusing on how it differs.

Python : download stock prices using the yfinance package

This post shows how to read prices of stock prices with a list of symbols as a string using Python. Splitting data by price types or symbols are illustrated as examples.

Python: Empirical Mode Decomposition

This post shows how to use Empirical Mode Decomposition (EMD), which decomposes a non-stationary univariate time series into several components of different time scales. These components are called intrinsic mode functions (IMFs).