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"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

machine-learning deep-learning text-analytics classification clustering natural-language-processing computer-vision data-science spacy nltk scikit-learn prophet time-series-analysis convolutional-neural-networks tensorflow keras statsmodels pandas deep-neural-networksFor deep learning, see our companion package: sktime-dl. The package is actively being developed and some features may not be stable yet.

data-science machine-learning data-mining time-series scikit-learn forecasting time-series-analysis time-series-classification time-series-regressionWhether you are an academic, data scientist, software developer, or time series enthusiast, STUMPY is straightforward to install and our goal is to allow you to get to your time series insights faster. See documentation for more information. Please see our API documentation for a complete list of available functions and see our informative tutorials for more comprehensive example use cases. Below, you will find code snippets that quickly demonstrate how to use STUMPY.

data-science pattern-matching pydata dask numba motif-discovery time-series-analysis anomaly-detection time-series-data-mining matrix-profile time-series-segmentationAnimated investment research at Sov.ai, sponsoring open source initiatives. Easily develop state of the art time series models to forecast univariate data series. Simply load your data and select which models you want to test. This is the largest repository of automated structural and machine learning time series models. Please get in contact if you want to contribute a model. This is a fledgling project, all advice appreciated.

finance time-series forecasting automated forecasting-models time-series-analysisUse Cases documentation demonstrates solutions to real-world data problems using Axibase Time Series Database (ATSD) and contains in-depth guides for programmatic integration with commonly-used enterprise software systems and services, as well as tutorials for data transformation and visualizations created with ATSD. Interactive visualizations tracking interesting datasets from a variety of sources.

dataset axibase atsd socrata open-data time-series statistical-analysis database visualization time-series-database time-series-analysistrck is a tool to query TrailDBs for aggregate metrics based on individual user behavior. trck is a domain specific language that defines a finite state machine1 to find patterns in data. These programs are compiled into highly optimized parallel native code.

traildb time-series-analysis discrete-event trck event-data big-data multicore state-machine compiler data-analyticsAll codes are using python 2.7. The source code are original work of the author, so any resemblance to existing work on the Internet would be merely coincidental. This work is free from every form of plagarism, so the references of the research papers used in writing the codes are provided.

kalman-filter signal-processing time-series-analysisThis is my bachelor's thesis that I wrote over the course of two months during my final year of studies, earning my Bachelor of Science in Computer Science degree. The thesis was co-authored by my good friend Tobias Ånhed. Click here for revised edition on DiVA.

lstm-neural-networks research-paper bachelor-thesis sequence-to-sequence machine-learning finance trading forex algorithmic-trading recurrent-neural-networks forex-trading technical-analysis technical-indicators artificial-neural-networks keras time-series-analysis financial-analysis white-paper publication trading-algorithmshctsa is a software package for running highly comparative time-series analysis using Matlab (full support for versions R2014b or later; for use in python cf. pyopy). The software provides a code framework that allows thousands of time-series analysis features to be extracted from time series (or a time-series dataset), as well as tools for normalizing and clustering the data, producing low-dimensional representations of the data, identifying discriminating features between different classes of time series, learning multivariate classification models using large sets of time-series features, finding nearest matches to a time series of interest, and a range of other visualizations and analyses.

time-series time-series-analysis matlab feature-extractionWelcome! The Financial Asset Comparison Tool is an R Shiny App that facilitates the comparison of a myriad of assets--both traditional and crypto--across time. The idea for this tool came to me when I was trading crypto-currencies actively, and spending a decent amount of time in investor telegram chats and forums. A common argument I would see was over what asset one should have invested in a short while ago, but it was clear that most such discussions were fueled by emotion--primarily "FOMO"--as opposed to testable metrics. This isn't just a popular type of discussion in the crypto investing space; in fact, it may be even more common in traditional finance. I wanted to create a tool that would be able to settle all such asset performance comparison questions, regardless of whether the question was about traditional assets such as equities, crypto-assets like Bitcoin and Ethereum, or some combination of both. The tools made available via this app allow for analysis of varying degrees of complexity, as can be seen in the visualization below. This scaling of metric complexity is also intuitively integrated into the UI design of the app itself, as illustrated by the screenshot below.

r quantmod performance-analysis shinydashboard shiny cryptocurrency financial-analysis equities stock-market stock-analysis risk-analysis investment-analysis portfolio-analysis bitcoin ethereum gdax coinbase plotly shiny-apps time-series-analysisTSrepr is R package for fast time series representations and dimensionality reduction computations. Z-score normalisation, min-max normalisation, forecasting accuracy measures and other useful functions implemented in C++ (Rcpp) and R.

time-series-analysis time-series-data-mining time-series-classification time-series-clustering representation data-science data-analysis data-mining-algorithms data-mining time-series r-package time-series-representations rSolutions to the problems in Time Series Analysis with Applications in R

r time-series time-series-analysis statisticsWelcome to my GitHub repo. I am a Data Scientist and I code in R, Python and Wolfram Mathematica. Here you will find some Machine Learning, Deep Learning, Natural Language Processing and Artificial Intelligence models I developed.

r python3 python-3 mathematica lasagne theano theano-models autoencoder face-recognition natural-language-processing nlp nlp-machine-learning deep-learning keras lstm lstm-neural-networks timeseries time-series-analysis word2vecData Mining is time series prediction engine for Hawkular. It autonomously selects best model for time series being modelled. Produced prediction can be used for alert prediction or in predictive charts in UI. To run Hawkular with Data Mining, clone and build Hawkular branch datamining and run the server. Predictive charts are located in Explorer tab.

timeseries time-series-analysis time-series-forecast forecasting-models forecasting exponential-smoothingKennedy, R.E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W.B., Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sensing. 10, 691.

landtrendr google-earth-engine time-series-analysis landsat change-detection landscape-dynamics mappingBitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network

lstm-neural-networks time-series-analysis bitcoin-price-prediction recurrent-neural-networks deep-neural-networks deep-learning-tutorial deep-learning series lstm rnn keras tensorflowOutlier Detection , also known as Anomaly Detection is a fascinating and useful technique to identify outlying data objects. It has been proven critical in many fields, such as credit card fraud analytics and mechanical unit defect detection. Outlier Ensembles: An Introduction by Charu Aggarwal and Saket Sathe: Great intro book for ensemble learning in outlier anaysis.

outlier-detection anomaly-detection outlier outlier-ensembles time-series-analysisChronikis (kroh-NEE-kees) is a special-purpose language for creating time-series models. It comes with a compiler chronikisc and an R package chronikis that contains utilities for calling the compiler as well as estimating and forecasting with the compiled time-series models. The name "Chronikis" is derived from the phrase χρονική σειρά (chronikí seirá), which means "time series" in Greek.

time-series-analysis bayesian-data-analysis modeling-languageTools for time series analysis, statistics and autoregressive models

statistics linear-regression data-analysis time-series-analysis arima autoregressiveChronetic is an open-source time pattern analysis library built to describe time-series data. Written in Java, using Jenetics, an advanced genetic algorithm; Chronetic is able to locate the most prevalent patterns occuring in a given time-series dataset. Patterns are aggregated into a Chronotype and can be translated into a human-readable format with a ChronoDescriptor. Visit http://chronetic.io/javadoc/ for the latest and most up-to-date JavaDoc documentation.

influxdb time-series time-series-analysis time-series-classification
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