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Exponential Smoothing - Time-series Analysis
Exponential Smoothing is another technique to smooth the time series data to get an overall long-term impression of the movements present in the data. As its name suggests, it is exponentially weighted moving averages.
http://www.techinfoplace.com/exponential-smoothing
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Check out my most recent paper on ArXiv about aligning two time-series together using a new method of Dynamic Time Warping (DTW). Code will be available on Github soon. The top plot shows two signals that we seek to align by warping time. The middle plot shows the mapping between the two signals defined by the time warping function, phi. The bottom plot shows how the two signals look when we compose signal x with the time warping function; it aligns beautifully with y! You can also do this with groups of signals. All of this (and more) is in the paper.
Time-Series Forecasting at Scale (°) Via #Facebook, it's really simple to use - you pass in your data, and it generated the forecast, automatically correcting for seasonality, outliers, missing data, shifts, and trends. Mind you, its best for daily periodicity, and comes with the ability to tweak, well, most things (°°) Interfaces in R and Python, with the base engine being Stan(http://mc-stan.org) It's good to see this kind of #OSS making it into the world from the giants 🙌 (°) Scale: Not data size - it's more about the size and variety of the problem space. (°°°) (°°) https://www.rdocumentation.org/packages/prophet/versions/0.1/topics/prophet (°°°) Paper here - https://facebookincubator.github.io/prophet/static/prophet_paper_20170113.pdf
In this article, Dr. Josiah Carlson, author of the book “Redis in Action”, explains how to use Redis and sorted sets with hashes for time series analysis.
An introduction to Faceboook's time series database
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Gnocchi and ceilometer links
Danjou on the origins of Gnocchi: http://opensource.com/business/14/10/interview-julien-danjou-enovance
Greater detail from Danjou on Gnocchi and the possible time-series dB backends. The distinction between events and metrics. The tsdb is for metering: https://julien.danjou.info/blog/2014/openstack-ceilometer-the-gnocchi-experiment
InfluxDB: DB for All Times
InfluxDB
InfluxDB is a time series, events, and metrics database. It's written in Go and has no external dependencies. That means once you install it there's nothing else to manage (like Redis, HBase, or whatever). It's designed to be distributed and scale horizontially, but be useful even if you're only running it on a single box.
Design Goals
Stores metrics data (like response times and cpu load. e.g. what you'd put into Graphite).
Stores events data (like exceptions, user analytics, or business analytics).
HTTP(S) interface for reading and writing data. Shouldn't require additional server code to be useful directly from the browser.
Security model that will enable user facing analytics dashboards connecting directly to the HTTP API.
Horizontally scalable.
On disk and in memory. It shouldn't require a cluster of machines keeping everything in memory since most analytics data is cold most of the time.
Simple to install and manage. Shouldn't require setting up external dependencies like Zookeeper and Hadoop.
Ability to compute percentiles and other functions on the fly.
Ability to automatically downsample data on different windows of time.
Can efficiently and automatically clear out raw data daily to free up space. However, some time series will keep data forever, while others will keep only a rolling window (like the last 30 days)
Should be able to add on custom real-time and batch analytics processing.
Pubsub interface
Ability to automatically expand storage, but adding storage nodes is not something that is done frequently. However, should make node replacement quick and easy.
Highly elastic to expand computing capacity (compute nodes should scale up and down quickly)
