# Tag: Time Series (26)

**Automated Feature Engineering for Time Series Data**- Nov 20, 2017.

We introduce a general framework for developing time series models, generating features and preprocessing the data, and exploring the potential to automate this process in order to apply advanced machine learning algorithms to almost any time series problem.**Top 6 errors novice machine learning engineers make**- Oct 30, 2017.

What common mistakes beginners do when working on machine learning or data science projects? Here we present list of such most common errors.**DeepSense: A unified deep learning framework for time-series mobile sensing data processing**- Aug 2, 2017.

Compared to the state-of-art, DeepSense provides an estimator with far smaller tracking error on the car tracking problem, and outperforms state-of-the-art algorithms on the HHAR and biometric user identification tasks by a large margin.**What Data You Analyzed – KDnuggets Poll Results and Trends**- Apr 26, 2017.

Image/video data analysis is surging, JSON replacing XML, anonymized data usage is growing in US and Europe (but not in Asia), itemsets and Twitter analysis is declining - some of the highlights of KDnuggets Poll on data types used.**Time Series Analysis with Generalized Additive Models**- Apr 18, 2017.

In this tutorial, we will see an example of how a Generative Additive Model (GAM) is used, learn how functions in a GAM are identified through backfitting, and learn how to validate a time series model.**Introduction to Anomaly Detection**- Apr 3, 2017.

This overview will cover several methods of detecting anomalies, as well as how to build a detector in Python using simple moving average (SMA) or low-pass filter.**Visualizing Time-Series Change**- Mar 9, 2017.

When creating time-series line charts, it’s important to consider which of the following messages you would like to communicate: Actual value of units? Change in absolute units? Percent change? Change from a specific point in time?**Time Series Analysis: A Primer**- Jan 17, 2017.

Time series analysis is a complex subject but, in short, when we use our usual cross-sectional techniques such as regression on time series data, variables can appear "more significant" than they really are and we are not taking advantage of the information the serial correlation in the data provides.**Introduction to Forecasting with ARIMA in R**- Jan 16, 2017.

ARIMA models are a popular and flexible class of forecasting model that utilize historical information to make predictions. In this tutorial, we walk through an example of examining time series for demand at a bike-sharing service, fitting an ARIMA model, and creating a basic forecast.**Combining Different Methods to Create Advanced Time Series Prediction**- Nov 16, 2016.

The results from combining methods for time series prediction have been quite promising. However, the degree of error for long-term predictions is still quite high. Sounds like a challenge, so some new experiments are forthcoming!**The Great Algorithm Tutorial Roundup**- Sep 20, 2016.

This is a collection of tutorials relating to the results of the recent KDnuggets algorithms poll. If you are interested in learning or brushing up on the most used algorithms, as per our readers, look here for suggestions on doing so!**A simple approach to anomaly detection in periodic big data streams**- Aug 24, 2016.

We describe a simple and scaling algorithm that can detect rare and potentially irregular behavior in a time series with periodic patterns. It performs similarly to Twitter's more complex approach.**KxCon2016, International kdb+ programmer conference, May 19-22, Montauk, NY**- Apr 22, 2016.

Kdb+ time-series database provides high performance analytics on very large-scale datasets. Kdb+ users and coders will gather for KxCon2016, 3 days of presentations and hands-on workshops.**Deriving Better Insights from Time Series Data with Cycle Plots**- Mar 9, 2016.

Visualization plays key role in analysis of time series data, to understand underlying trends. Here we are demonstrating the cycle plot which shows both the cycle or trend and the day-of-the-week or the month-of-the-year effect.**Anomaly Detection in Predictive Maintenance with Time Series Analysis**- Dec 9, 2015.

How can we predict something we have never seen, an event that is not in the historical data? This requires a shift in the analytics perspective! Understand how to standardization the time and perform time series analysis on sensory data.**Data-Planet Statistical Datasets**- Nov 4, 2015.

Data-Planet Statistical Datasets provides easy access to an extensive repository of standardized and structured statistical data, with more than 25 billion data points from more than 70 source organizations.**Top KDnuggets tweets, Jul 21-27: Beginner Guide to Time Series Analysis; Free Deep Learning online course**- Jul 28, 2015.

Beginner #Guide to #TimeSeries #Analysis; Nvidia free #online course: Intro to #DeepLearning ; To Code or Not to Code with @KNIME; Guide To Linear #Regression**Top KDnuggets tweets, Mar 23-25: 24 free resources on Data Mining, Data Science; More Training Data or More Complex Models?**- Mar 26, 2015.

24 free resources and online books on #DataMining, #DataScience, #MachineLearning; New R Online Tool for Seasonal Adjustment of time series; Key #DataScience question: More Training Data or More Complex Models?; Twitter #DataMining finds origins of ISIS support.**Top stories for Feb 1-7: Avoiding a Common Mistake with Time Series; Top Big Data Influencers and Brands**- Feb 8, 2015.

Avoiding a Common Mistake with Time Series; (Deep Learning Deep Flaws) Deep Flaws; Top Big Data Influencers and Brands; Two Most Important Trends in Analytics and Big Data.**Top KDnuggets tweets, Feb 2-3: Avoiding a Common Mistake with Time Series; A New Year in Data Science, great overview**- Feb 4, 2015.

Avoiding a Common Mistake with Time Series: use de-trending; A New Year in #DataScience, great overview of the #MachineLearning and #BigData; Data scientist memes - the 'hottest profession'; Top Big Data Influencers and Brands.**KDnuggets™ News 15:n04, Feb 4: Top Big Data Influencers; A Common Mistake with Time Series; Ayasdi**- Feb 4, 2015.

Top Big Data Influencers and Brands; K-means clustering is not a free lunch; Avoiding a Common Mistake with Time Series; Ayasdi: Managing Data Complexity through Topology; Big Data Could Revolutionize Healthcare.**Avoiding a Common Mistake with Time Series**- Feb 2, 2015.

We explore a common mistake in analyzing relationships between time series, and show how de-trending helps to avoid this error.**“Vite fait, bien fait” – Averaging improves both accuracy and speed of time series classification**- Dec 21, 2014.

Time series classification using k-nearest neighbors and dynamic time warping can be improved in many practical applications in both speed and accuracy using averaging.**SPOTLIGHT: Can Data Science Save Humanity from Mosquitoes and other Deadly Insects? #2**- Oct 9, 2014.

KDnuggets launches Spotlight initiative to bring attention to academic research. The journey begins with Prof. Eamonn Keogh, UCR and his talented student, Yanping Chen, who are applying data mining to save us all from insect-vectored diseases.**SPOTLIGHT: Can Data Science Save Humanity from Mosquitoes and other Deadly Insects?**- Oct 8, 2014.

KDnuggets launches Spotlight initiative to bring attention to academic research. The journey begins with Prof. Eamonn Keogh and his student, Yanping Chen, who are applying data mining to save us all from insect-vectored diseases.**Interview: Leo Meyerovich, Graphistry on Browser-based Interactive Big Data Visualization**- Jul 24, 2014.

We discuss the merits of Superconductor architecture, comparison with current JavaScript visualization library, use cases, future plans, launch of Graphistry, visualization trends, and more.