Time series is a sequence of numerical data points in successive order and time series analysis is the technique of analysing the available data to predict the future outcome of an application. At present, time series analysis has been utilised in a number of applications, including stock market analysis, economic forecasting, pattern recognition, and sales forecasting. Here is a list of top ten Python tools, in no particular order, for Time Series Analysis. About: Arrow is a Python library that offers a human-friendly approach to creating, manipulating, formatting and converting dates, times and timestamps. The library implements and updates the datetime type, plugging gaps in functionality and providing an intelligent module API that supports many common creation scenarios. About: Cesium is an open source library that allows users to extract features from raw time series data, build machine learning models from these features, as well as generate predictions for new data.

Feature Engineering is a crucial step in many machine learning projects, but can be difficult and time consuming if you aren't already deeply familiar with the data and/or domain. So when I came across the FeatureTools framework, which promises to make Feature Engineering faster and easier, I was excited to try it out. FeatureTools allows you to setup Entities and relationships in your data and can then automatically generate tens to hundreds of new features for you. I jumped in to try to use FeatureTools on the Ames Housing Data set, which seemed ideal for Feature Engineering. However, I was getting some strange results and so decided to back off and try it out on the much simpler Titanic data set.

A normalized, relational dataset makes it easier to perform feature engineering. Unfortunately, raw data for machine learning is often stored as a single table, which makes the normalization process tedious and time-consuming. Well, I am happy to introduce you to AutoNormalize, an open-source library that automates the normalization process and integrates seamlessly with Featuretools, another open-source library for automated feature engineering. The normalized dataset can then be returned as either an EntitySet or a collection of DataFrames. Using AutoNormalize makes it easier to get started with Featuretools and can help provide you with a quick preview of what Featuretools is capable of. AutoNormalize also helps with table normalization, especially in situations when the normalization process is not intuitive.

This one line of code gives us over 200 features for each label in cutoff_times. Each feature is a combination of feature primitives and is built with only data from before the associated cutoff time. The features built by Featuretools are explainable in natural language because they are built up from basic operations. For example, we see the feature AVG_TIME_BETWEEN(transactions.transaction_date). This represents the average time between transactions for each customer. When we plot this colored by the label we see that customers who churned appear to have a slightly longer average time between transactions. In addition to getting hundreds of valid, relevant features, developing an automated feature engineering pipeline in Featuretools means we can use the same code for different prediction problems with our dataset. We just need to pass in the correct label times to the cutoff_times parameter and we'll be able to build features for a different prediction problem.

This is the fourth in a four-part series on how we approach machine learning at Feature Labs. These articles cover the concepts and a full implementation as applied to predicting customer churn. The project Jupyter Notebooks are all available on GitHub. All of the work documented here was completed with open-source tools and data.) The Machine Learning Modeling ProcessThe outputs of prediction and feature engineering are a set of label times, historical examples of what we want to predict, and features, predictor variables used to train a model to predict the label.

Anyone who has participated in machine learning hackathons and competitions can attest to how crucial feature engineering can be. It is often the difference between getting into the top 10 of the leaderboard and finishing outside the top 50! I have been a huge advocate of feature engineering ever since I realized it's immense potential. But it can be a slow and arduous process when done manually. I have to spend time brainstorming over what features to come up, and analyze their usability them from different angles. Now, this entire FE process can be automated and I'm going to show you how in this article.

There are few certainties in data science -- libraries, tools, and algorithms constantly change as better methods are developed. However, one trend that is not going away is the move towards increased levels of automation. Recent years have seen progress in automating model selection and hyperparameter tuning, but the most important aspect of the machine learning pipeline, feature engineering, has largely been neglected. The most capable entry in this critical field is Featuretools, an open-source Python library. In this article, we'll use this library to see how automated feature engineering will change the way you do machine learning for the better.

Developers at MIT and Spanish bank BBVA used Featuretools to build features to train better fraud detection models. Implements DFS for automated feature engineering. It works to prepare raw relational and transactions datasets for machine learning or predictive modeling. Includes a collection of reusable feature engineering functions for a wide range of domains. Allows for user-defined primitives to encourage automation, reuse across projects, and community collaboration. Designed to work with common frameworks like Pandas for data preparation or scikit-learn for machine learning.

Today, when an enterprise wants to use machine learning to solve a problem, they have to call in the cavalry. Even a simple problem requires multiple data scientists, machine learning experts, and domain experts to come together to agree on priorities and exchange data and information. This process is often inefficient, and it takes months to get results. It also only solves the problem immediate at hand. The next time something comes up, the enterprise has to do the same thing all over again.

The artificial intelligence market is fueled by the potential to use data to change the world. While many organizations have already successfully adapted to this paradigm, applying machine learning to new problems is still challenging. The single biggest technical hurdle that machine learning algorithms must overcome is their need for processed data in order to work -- they can only make predictions from numeric data. This data is composed of relevant variables, known as "features." If the calculated features don't clearly expose the predictive signals, no amount of tuning can take a model to the next level.