The fundamental package for AIops with python.

Electricity demand forecasting for Austin, TX, using a combination of timeseries methods and regression models

Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data.

TensorFlow in Practice Specialization. Join our Deep Learning Adventures community 🎉 and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting 😀 All while having fun learning and participating in our Deep Learning Trivia games 🎉 http://bit.ly/deep-learning-tf

Client for Microprediction.Org

SSIM - A Deep Learning Approach for Recovering Missing Time Series Sensor Data

Deep Learning Adventures. Join our Deep Learning Adventures community 🎉 and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting 😀 All while having fun learning and participating in our Deep Learning Trivia games 🎉 http://bit.ly/deep-learning-tf

This project gives an overview of crime time analysis in New York City . We have created Python Jupyter notebooks for spatial analysis of different crime types in the city using Pandas, Numpy, Plotly and Leaflet packages. As a second part to this analysis, we worked on ARIMA model on R for predicting the crime counts across various localities in the city based on correlations of various demographics correlation in each locality.

A Long Short Term Memory neural network for time series prediction. Memory blocks contain one memory cell in each. Weights for the network are randomly initialized.

Anomaly Detection in R - the tidy way using anomalize

Performant, composable online learning

a classification model that classify whether the EURUSD stock exchange will go up or down next day based on historical data

Exploring HMM, LSTM and Regression techniques to predict respiratory rate of an individual from accelerometer data.

This repository implements a Recurrent Neural Network, which predicts the spread of COVID-19 accross the world using as training data only the nearest neighbors in terms of growth.

The exploratory data analysis for the road traffic data-sets used in the Traffic Congestion project.

Udacity Deep Learning Nanodegree Project 1 on bike-sharing patterns. Do 🌟 it and show some love

A Demo for RedisGears function that uses fbprophet for timeseries prediction

time series forecasting

AdaHMG: A first-order stochastic optimization algorithm for time series data

Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).

Shiny app for FSN model comparison

Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution

Multivariate time-series forecasting with LSTNET and soft-DTW loss

Some of the ML codes I have come across

Forecasting the growth of GitHub repositories (in Python and R languages) over the next 5 years.

A service to help you analyze long term trends and generate forecasting for your metrics/data

Analytics using Microsoft Power BI to forecast sales of healthcare products using ARIMA forecast model.

A self-driven project utilizing ARIMA, Seq2Seq, and XGBoost to help design the COVID19 forecasting algorithm. Data sources are from Kaggle Competition and JHU CSSE.

Analyzing and Predicting The COVID-19 Global Cases As Well As On The Basis Of Every Possible Individual Country Using Tensorflow and Python