Time Series Forecasting Best Practices & Examples

List of papers, code and experiments using deep learning for time series forecasting

Curated list of awesome supply chain blogs, podcasts, standards, projects, and examples.

This project is a collection of recent research in areas such as new infrastructure and urban computing, including white papers, academic papers, AI lab and dataset etc.

Bike sharing prediction based on neural nets

Dynamic Bandwidth Monitor; leak detection method implemented in a real-time data historian

In tune with conventional big data and data science practitioners’ line of thought, currently causal analysis was the only approach considered for our demand forecasting effort which was applicable across the product portfolio. Experience dictates that not all data are same. Each group of data has different data patterns based on how they were sold and supported over the product life cycle. One-methodology-fits-all is very pleasing from an implementation of view. On a practical ground, one must consider solutions for varying needs of different product types in our product portfolio like new products both evolutionary and revolutionary, niche products, high growth products and more. With this backdrop, we have evolved a solution which segments the product portfolio into quadrants and then match a series of algorithms for each quadrant instead of one methodology for all. And technology stack would be simulated/mocked data(Hadoop Ecosystem) > AzureML with R/Python > Zeppelin.

E-commerce Inventory System developed using Vue and Vuetify

Minimize forecast errors by developing an advanced booking model using Python

Model ogólny mobilności miejskiej dla miast małych i średnich - do celów dydaktycznych, badawczych i innych (c) Rafal Kucharski, Politechnika Krakowska, 2018

Stanford Continuing Studies course BUS 150 W "Financial Modeling and Business Decisions" by Iddo Hadar, Summer 2017. This online course provides students with the methods and the mindset to make complex financial and economic decisions, by framing them as analytical models and using Microsoft Excel spreadsheets to solve them.

Forecasting the Demand of __ using various time series methods

R package for the Station Demand Forecasting Tool

Forecasting Food Sales in a Multiplex Using Dynamic Artificial Neural Networks

In tune with conventional big data and data science practitioners’ line of thought, currently causal analysis was the only approach considered for our demand forecasting effort which was applicable across the product portfolio. Experience dictates that not all data are same. Each group of data has different data patterns based on how they were sold and supported over the product life cycle. One-methodology-fits-all is very pleasing from an implementation of view. On a practical ground, one must consider solutions for varying needs of different product types in our product portfolio like new products both evolutionary and revolutionary, niche products, high growth products and more. With this backdrop, we have evolved a solution which segments the product portfolio into quadrants and then match a series of algorithms for each quadrant instead of one methodology for all. And technology stack would be simulated/mocked data(Hadoop Ecosystem) > AzureML with R/Python > Zeppelin.

Atom syntax highlighting for GISDK scripts

Demand pattern recognition using k-means algorithm in Python

Grupo Bimbo inventory demand Kaggle competition

An exploration of demand analysis and prediction

Big Data Inventory Management on AWS (Demand Forecasting, Machine Learning, Dashboarding) : Presented at Carlson School of Management during the Trends Marketplace event to professors, alumni and working professionals from various companies.

Solution to the Data Mining Cup 2020 competition

Bike share demand : Forecast city bikeshare demand with prophet

Demand Forecasting through SARIMA and Winter Holt's Methods

📘LBF presentation on demand forecasting models

Predicting bike sharing demand patterns with a feed-forward neural network implemented in raw NumPy.