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sktime – Python Toolbox for Machine Learning with Time Series
Editor’s note: Franz Kiraly is a speaker for ODSC Europe this June. Be sure to check out his talk, “sktime – Python Toolbox for Machine Learning with Time Series,” there! Welcome to sktime, the open community and Python framework for all things time series. Here’s what... Read more
Simplifying Time Series Forecasting: Replicating Monsaraida’s Solution on Kaggle for Retail Volume Predictions
The M5 Competition, hosted on Kaggle, has recently drawn attention to the effectiveness of gradient-boosting methods for volume forecasts of retail products. In this article, we will focus on the accuracy track of the competition and tackle a time series problem. By replicating one of the... Read more
Leveraging Time-Series Segmentation and Machine Learning for Better Forecasting Accuracy
Several papers discussed the importance of segmenting time series into groups and modeling each group separately to enhance forecasting accuracy overall. But what does this look like in practice? At the end of the day, why not use an AutoML package (Automated Machine Learning) or an... Read more
Recurrent Neural Networks for Financial Time Series Prediction
Editor’s note: Nicole Königstein is a speaker for ODSC Europe 2022. Be sure to check out her talk, Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment, there to learn more about financial time series prediction! The use of neural networks is relatively... Read more
Google AI Proposes Temporal Fusion Transformer for Multi-Horizon Time Series Forecasting
Time series forecasting is a useful data science tool for helping people predict what will happen in the future based on historical, time-stamped information. Google researchers recently explained how they developed and used the company’s Temporal Fusion Transformer (TFT) to achieve more progress with these types... Read more
Show Me the Data: 8 Awesome Time Series Sources
Thanks to the Internet of Things, smart cities, e-health, autonomous machines, and other innovations, time series datasets are being produced in even more massive quantities. It can be used for econometrics, trend detection, pattern recognition, predictions, and is an essential ingredient in statistics, machine learning, and... Read more
Facebook’s Prophet Forecasting Crystal Ball
Facebook’s Prophet is one of the most-liked forecasting approaches nowadays. Its usage is very well described, the code itself cleanly documented, hence instead of giving examples of Facebook’s Prophet, we will look under the hood to understand where these Bayesian model novelties lie. The Model The... Read more
Machine Learning for Time Series Data
Most organizations generate time-series data. The generation of sales data and financial data are primary components of all organizations’ business. This data is a form of time series data. Time series data consists of any data that carries a temporal component with it. Time series data... Read more
A Practitioner’s Guide To Interrupted Time Series
In the world of causal inference, Randomized Controlled Trials, RCTs, are considered the gold standard as it rules out any covariate differences before the intervention. However, running an RCT isn’t an option for multiple reasons (e.g., too expensive, invalid assumptions, too long, not ethical, etc.). [Related... Read more
Interpreting the 2020 Puerto Rico Earthquake Swarm with Data Science
Using visualizations, maps, time series and Google Trends data science, the 2020 Puerto Rico earthquake swarm is described. Since late December 2019 until early January 2020, the southwestern region of the island Puerto Rico has been experiencing a series, or swarm, of earthquakes, leaving in its... Read more