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fitting-probability-distribution

When dealing with any type of dataset it is important to create a probabilistic model of the data

flow-forecast

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

fluke289_data_access

A series of Python scripts to access measurements from Fluke 28X meters. Fluke IR Remote Interface required.

fluke_scopemeter_matlab

Communication with Fluke Oscilloscope "scope meter" via Matlab

forecast-ai

A bitcoin and stock price forecaster utilizing an ensemble of autoregressive, N-BEATS, LSTM and layer normalized models, trained on various loss functions in TensorFlow. Utilizing Java for implementing a front-end interface for the application.

forecast_evaluation

Methods and tools for evaluating probabilistic forecasts

forecast_the_geomagnetic_storms

In this project, the task will be to forecast Dst in real-time to help satellite operators, power grid operators, and users of magnetic navigation systems prepare for magnetic disturbances. We used a DNN algorithm implemented in Python with Keras.

forecasting

Time-series forecasting with Deep Learning: RNN, LSTM, GRU, Rolling RNN/LSTM/GRU

forecasting-and-classification-of-mechanical-faults

predictive-maintenance-fault-classification(CWRU data)-and-remaining-useful-life(NASA’s Turbofan Engine )

forecasting-method-based-upon-gru-based-deep-learning-model

In this World Model, we specifically modified the RNN model by replacing the LSTM model with Bidirectional GRU. A typical advantage of using GRU is that it has less training parameters as compared to LSTM which eventually trains faster.

forecasting-sunspot-time-series-using-deep-learning.-

The number of sunspots increases and decreases over time in a regular, approximately 11-year cycle, called the sunspot cycle. The exact length of the cycle can vary. In this project, we study this time series using neural networks.

forecasting-temperature-bidirectional-lstm

Bidirectional LSTM model to forecast the minimum temperature on a daily level

forecasting-the-total-daily-covid--19-vaccination-accross-the-world

Performed EDA and introduced the Box-Cox transformation to stabilize the variance and made the time series

forecasting-with-time-series-imaging

time series forecasting with image

forecasting_air_pollution

Stacking a machine learning ensemble for multivariate time series forecasting, with the goal of predicting the one-period ahead PM 2.5 air pollution level, as published in Towards Data Science on Medium.com

forecasting_heatload

This projects develops several SARIMAX time series models and neural networks (LSTM, GRU, CNN) to predict the hourly heat demand in a district heating network.

foss_lab4_anomaly_detection_time_series_keras

This is a repository for Foss lab project where anomilies has been detected in time series data model using S&P_500 index dataset

framework-data

What Drives Academic Data Sharing?

frols-algo

FROLS Algorithm to Fit an NARX Model

fruit-images-dataset

Fruits-360: A dataset of images containing fruits and vegetables

fseval

Benchmarking framework for Feature Selection and Feature Ranking algorithms 🚀

ft2wt-examples

Examples of "From Fourier Transform to Wavelet Transform"

fts2image

Forecasting model using Fuzzy Time Series and Deep Neural Networks

fuzzy-forecast

Fuzzy model for time-series forecasting

fuzzy-time-series-methods-applied

Solar forecasting

fuzzy-time-series-model

Use the fuzzy time series model to forecast the future based on the history data

fuzzy_logic

Membership functions, fuzzy time series, fuzzy time series, time series forecasting, Song model. Charts. Django.

gdn

Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series" (AAAI 2021)

gdrive

A Matlab class to simplify uploading and downloading data to Google Drive. Relies on the gdrive tools for the actual interaction with Google Drive.

gearbox-fault-analysis-usign-ml