Predict the future — generic data transformation and feature engineering basics. In this article, I’m going to show a generic way how to approach a time-series prediction problem.
In this article, I’m going to show a generic way how to approach a time-series prediction problem with a machine learning model. I’m using a tree-based model LightGBM as an example here, but practically it could be almost any classical machine learning model, including linear regression or some other. I’ll cover needed data transformation, basic feature engineering and, modeling itself.
Time series is basically a list of values at known points in time. Some examples: daily purchases at a store, the daily number of website visitors, hourly temperature measurements, etc. Often the interval between data points is fixed, but that is not a mandatory requirement. And the problem we want to solve in a time series is to predict the value at points in the future.
There are many specialized ways how to approach this problem. Exponential smoothing, special models like ARIMA, Prophet, and of course Neural Networks (RNN, LSTM), just to name some. But in this article, I will focus on using generic tree-based models for time series forecasting.
Flow-Forecast: A time series forecasting library built in PyTorch. Accurate multivariate time series forecasting and classification remains central challenge for many businesses and non-profits.
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.
How to use Deep Learning for Time Series Forecasting. An application of the RNN family
This article was written by Rosana de Oliveira Gomes on behalf of the Deep Delve team for the AI4Impact Deep Learning Datathon 2020.
An intuitive take on sales forecasting from traditional time series models to modern deep learning. In any company, there is an embedded desire to predict its future revenue and future sales. The basic recipe is: Collect historical data related to previous sales and use it to predict expected sales.