This Blog is based on my talk at the International Symposium on Forecasting ISF2020, titled — Best Practices for Scaling Sales Forecasting.
At Wix.com for the last few years we’ve been using time-series forecasting models as part of our data science projects for forecasting Wix’s future collections. This allowed the company two important things: (1) Better budget planning based on future collections; (2) Accurate guidance to the stock market.
Forecasting collections is a challenging task (every forecasting task is) but it’s one we’ve constantly improved on and achieved amazing results. In this blog I want to share some of our insights and practices for scaling a forecasting project.
For our system to scale and accommodate more features, more models and eventually more forecasters we divided it to the above building blocks. Each building block stands on its own but also knows how to communicate and work with others. A short explanation of each (hopefully I would describe them in more depth at the future, comment below/DM with requests):
As mentioned, this design supports scaling and as important it supports fast experimentation which is the basis of every successful data science project.
How to use Deep Learning for Time Series Forecasting. An application of the RNN family
In this article, we will be discussing an algorithm that helps us analyze past trends and lets us focus on what is to unfold next so this algorithm is time series forecasting. In this analysis, you have one variable -TIME. A time series is a set of observations taken at a specified time usually equal in intervals. It is used to predict future value based on previously observed data points.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
While LSTMs have become increasingly popular for time series analysis, they do have limitations. Long-short term memory networks (LSTMs) are now frequently used for time series analysis.