What the heck is time-series data (and why do I need a time-series database)?

@tachyeonz : Here’s a riddle: what do self-driving Teslas, autonomous Wall Street trading algorithms, smart homes, transportation networks that fulfill lightning-fast same-day deliveries, and an open-data-publishing NYPD have in common?

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Writing a Time Series Database from Scratch

@tachyeonz : I work on monitoring. In particular on Prometheus, a monitoring system that includes a custom time series database, and its integration with Kubernetes. In many ways Kubernetes represents all the things Prometheus was designed for.

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Timescale

@tachyeonz : How we scaled SQL Time-series workloads are different. TimescaleDB introduces special partitioning and distributed query optimizations to unlock new possibilities for SQL. Learn more Ease of use Query with standard SQL. Connect to tools that speak standard database connections.

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What’s Wrong With My Time Series

@tachyeonz : What’s wrong with my time series? Model validation without a hold-out set Time series modeling sits at the core of critical business operations such as supply and demand forecasting and quick-response algorithms like fraud and anomaly detection.

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Prophet: How Facebook operationalizes time series forecasting at scale

@tachyeonz : Facebook is a famously data-driven organization, and an important goal in any data science activity is forecasting. Now, Facebook has released Prophet, an open-source package for R and Python that implements the time-series methodology that Facebook uses in production for forecasting at scale.

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Time Series Analysis (TSA) in Python – Linear Models to GARCH

@tachyeonz : Early in my quant finance journey, I learned various time series analysis techniques and how to use them but I failed to develop a deeper understanding of how the pieces fit together.

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Plotting timeseries in space filling curves

@tachyeonz : Fitting many timeseries in the same area and formatting them for quick comparison is challenging and an important problem.

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How seasonal components can be represented as sinusoids in a regression model.

@tachyeonz : There was a verbal solution given to this problem in the members only section. I’m not sure if its “legal” to share the whole thing, but here is an excerpt of the solution.

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How signal processing can be used to identify patterns in complex time series

@tachyeonz : The trend and seasonality can be accounted for in a linear model by including sinusoidal components with a given frequency. However, finding the appropriate frequency for each sinusoidal component requires a little more digging.

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NIPS 2016 Review, Day 1

@tachyeonz : Ever the scientists, the two organizers justified their choice on the program committee by maintaining that they want to grow the number submissions while decreasing bias and variance. They treated the problem with unknown ground truth of what the “best papers” were,

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Tags : artificial intelligence, cnn, conference, convolution neural net, deep learning, gans, lstm, m, machine learning, meta learning, meta models, nips2016, phased lstm, recurrent neuralnet, reinforcement learning, rnn, time series data, unsupervised learning, yann le cun

Published On:December 25, 2016 at 07:26PM

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Plotting timeseries in space filling curves

@tachyeonz : Fitting many timeseries in the same area and formatting them for quick comparison is challenging and an important problem.

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Tags : data visualization, dataviz, glyphs, graphs, hilbert charts, m, plotting, python, time series data

Published On:December 25, 2016 at 01:52AM

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