Hello, friends. In this blog post, we will quickly peek through the package “Deep-XF” that is useful for forecasting, nowcasting, uni/multivariate time-series data analysis, filtering noise from time-series signals, comparing two input ts signals, etc. The USP of this package is its bunch of add-on utility helper functions, and the model explainability module that can be used for explaining model results, be it the forecasting/nowcasting problem.
easy to useand
low-codesolution. It enables users to perform end-to-end Proof-Of-Concept (POC) quickly and efficiently. One can build forecasting models based on deep neural networks such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional RNN/LSTM/GRU (BiRNN/BiLSTM/BiGRU), Spiking Neural Network (SNN), Graph Neural Network (GNN), Transformers, Generative Adversarial Network (GAN), Convolutional Neural Network (CNN), and others. It also provides facilities to build a nowcast model using Dynamic Factor Model based on Expectation-Maximization algorithm.
Deep-XF package includes:-
- Exploratory Data Analysis with facilities like profiling, filtering outliers, univariate/multivariate plots, plotly interactive plots, rolling window plots, detecting peaks, etc.
- Data Preprocessing for Time-series data with services like finding missing, imputing missing, date-time extraction, single timestamp generation, removing unwanted features, etc.
- Descriptive statistics for the provided time-series data, normality evaluation, etc.
- Feature engineering with services like generating time lags, date-time features, one-hot encoding, date-time cyclic features, etc.
- Finding similarity between homogeneous time-series signal inputs with Siamese Neural Networks.
- Denoising (Filtering noise) from time-series input signals.
- Building Deep Forecasting Model with hyperparameters tuning and leveraging available computational resources (CPU/GPU).
- Forecasting model performance evaluation with several key metrics
- Game theory based methods to interpret forecasting model results.
- Building Nowcasting model with Expectation–maximization algorithm
- Explainable Nowcasting