In the midst of the deep learning hype, p-values might not be the hottest topic in data science. However, association mapping remains a fundamental tool to justify and underpin scientific conclusions. Inspired by an approach for time series classification based on predictive subsequences (i.e shapelets [1]), we developed S3M, a method that identifies short time series subsequences that are statistically associated with a class or phenotype while tackling the multiple hypothesis problem.

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When developing IoT, financial or industrial applications, the choice of a good time series database is most of the time a headache…

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You will learn in this post how to:

- decompose double-seasonal time series
- detrend time series
- model and forecast double-seasonal time series with trend
- use two types of simple regression trees
- set important hyperparameters related to regression tree

This web site contains notes and materials for an advanced elective course on statistical forecasting that is taught at the Fuqua School of Business, Duke University. It covers linear regression and time series forecasting models as well as general principles of thoughtful data analysis.

The time series material is illustrated with output produced by Statgraphics, a statistical software package that is highly interactive and has good features for testing and comparing models, including a parallel-model forecasting procedure that I designed many years ago.

The material on multivariate data analysis and linear regression is illustrated with output produced by RegressIt, a free Excel add-in which I also designed. However, these notes are platform-independent. Any statistical software package ought to provide the analytical capabilities needed for the various topics covered here.

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This article focuses on using a Deep LSTM Neural Network architecture to provide multidimensional time series forecasting using Keras and Tensorflow - specifically on stock market datasets to provide momentum indicators of stock price.

The following article sections will briefly touch on LSTM neuron cells, give a toy example of predicting a sine wave then walk through the application to a stochastic time series. The article assumes a basic working knowledge of simple deep neural networks.

]]>When you are presented with a new time series forecasting problem, there are many things to consider.

The choice that you make directly impacts each step of the project from the design of a test harness to evaluate forecast models to the fundamental difficulty of the forecast problem that you are working on.

It is possible to very quickly narrow down the options by working through a series of questions about your time series forecasting problem. By considering a few themes and questions within each theme, you narrow down the type of problem, test harness, and even choice of algorithms for your project.

In this post, you will discover a framework that you can use to quickly understand and frame your time series forecasting problem.

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An in-depth look into how two leading time-series databases stack up against another.

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Since my first week on this platform, I have been fascinated by the topic of time series analysis. This kernel is prepared to be a container of many broad topics in the field of time series analysis. My motive is to make this the ultimate reference to time series analysis for beginners and experienced people alike.

- This kernel is a work in progress so every time you see on your home feed and open it, you will surely find fresh content.
- I am doing this only after completing various courses in this field. I continue to study more advanced concepts to provide more knowledge and content.
- If there is any suggestion or any specific topic you would like me to cover, kindly mention that in the comments.
- If you like my work, be sure to upvote(press the like button) this kernel so it looks more relevant and meaningful to the community.

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Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

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Twitter's Anomaly Detection in Pure Python

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