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Practical Financial Data Analysis With Python Data Science


Obtain & Work With Real Financial Data Get Coupon Code Hot & New What you'll learn LEARN To Obtain Real World Financial Data FREE From Yahoo and Quandl BE ABLE To Read In, Pre-process & Visualize Time Series Data IMPLEMENT Common Data Processing And Visualisation Techniques For Financial Data in Python LEARN How To Use Different Python-based Packages For Financial Analysis MODEL Time Series Data To Forecast Future Values With Classical Time Series Techniques USE Machine Learning Regression For Building Predictive Models of Stock prices LEARN How to Use Facebook's Powerful Prophet Algorithm For Modelling Financial Data IMPLEMENT Deep learning methods such as LSTM For Forecasting Stock Data Requirements Prior Familiarity With The Interface Of Jupiter Notebooks and Package Installation Prior Exposure to Basic Statistical Techniques (Such As p-Values, Mean, Variance) Be Able To Carry Out Data Reading And Pre-Processing Tasks Such As Data Cleaning In Python Interest In Working With Time Series Data Or Data With A Time Component To Them Description THIS IS YOUR COMPLETE GUIDE TO FINANCIAL DATA ANALYSIS IN PYTHON! This course is your complete guide to analyzing real-world financial data using Python. All the main aspects of analyzing financial data- statistics, data visualization, time series analysis and machine learning will be covered in depth. If you take this course, you can do away with taking other courses or buying books on Python-based data analysis. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal.

Deep Learning: the final Frontier for Time Series Analysis? - JAXenter


One important data type which includes time series, digital signals and any sequential observations is still mainly processed with rather standard mathematical and algorithmic routines. In this talk, we will review, what are the main sources of time series in the world, what are the "basic" algorithms and how exactly they might be improved and replaced with different neural network architectures. Apart from the models' details, we will also study the typical tasks that have to be solved while working with time series: classification, prediction, anomaly detection, simulation and others and exactly deep learning can be leveraged to solve them on the state-of-the-art level. Some previous experience with time series/signal processing is useful for getting the most out of this session, but not required. Alex Honchar is developing production-ready AI solutions for small and medium businesses for the last 5 years, giving public speeches in Europe and blogging about ML and AI recent advances.

Machine Learning Can't Handle Long-Term Time-Series Data - LessWrong 2.0


This may come as a surprise because computers seem like they can understand time series data. After all, aren't self-driving cars, AlphaStar and recurrent neural networks all evidence that today's ML can handle time series data? Self-driving cars use a hybrid of ML and procedural programming. ML (statistical programming) handles the low-level stuff like recognizing pedestrians. Procedural (nonstatistical) programming handles high-level stuff like navigation.

Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case Machine Learning

In this paper, we present a new approach to time series forecasting. Time series data are prevalent in many scientific and engineering disciplines. Time series forecasting is a crucial task in modeling time series data, and is an important area of machine learning. In this work we developed a novel method that employs Transformer-based machine learning models to forecast time series data. This approach works by leveraging self-attention mechanisms to learn complex patterns and dynamics from time series data. Moreover, it is a generic framework and can be applied to univariate and multivariate time series data, as well as time series embeddings. Using influenza-like illness (ILI) forecasting as a case study, we show that the forecasting results produced by our approach are favorably comparable to the state-of-the-art.

Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification Machine Learning

Time series motifs play an important role in the time series analysis. The motif-based time series clustering is used for the discovery of higher-order patterns or structures in time series data. Inspired by the convolutional neural network (CNN) classifier based on the image representations of time series, motif difference field (MDF) is proposed. Compared to other image representations of time series, MDF is simple and easy to construct. With the Fully Convolution Network (FCN) as the classifier, MDF demonstrates the superior performance on the UCR time series dataset in benchmark with other time series classification methods. It is interesting to find that the triadic time series motifs give the best result in the test. Due to the motif clustering reflected in MDF, the significant motifs are detected with the help of the Gradient-weighted Class Activation Mapping (Grad-CAM). The areas in MDF with high weight in Grad-CAM have a high contribution from the significant motifs with the desired ordinal patterns associated with the signature patterns in time series. However, the signature patterns cannot be identified with the neural network classifiers directly based on the time series.

Inference for Network Structure and Dynamics from Time Series Data via Graph Neural Network Machine Learning

Network structures in various backgrounds play important roles in social, technological, and biological systems. However, the observable network structures in real cases are often incomplete or unavailable due to measurement errors or private protection issues. Therefore, inferring the complete network structure is useful for understanding complex systems. The existing studies have not fully solved the problem of inferring network structure with partial or no information about connections or nodes. In this paper, we tackle the problem by utilizing time series data generated by network dynamics. We regard the network inference problem based on dynamical time series data as a problem of minimizing errors for predicting future states and proposed a novel data-driven deep learning model called Gumbel Graph Network (GGN) to solve the two kinds of network inference problems: Network Reconstruction and Network Completion. For the network reconstruction problem, the GGN framework includes two modules: the dynamics learner and the network generator. For the network completion problem, GGN adds a new module called the States Learner to infer missing parts of the network. We carried out experiments on discrete and continuous time series data. The experiments show that our method can reconstruct up to 100% network structure on the network reconstruction task. While the model can also infer the unknown parts of the structure with up to 90% accuracy when some nodes are missing. And the accuracy decays with the increase of the fractions of missing nodes. Our framework may have wide application areas where the network structure is hard to obtained and the time series data is rich.

TimeCaps: Capturing Time Series Data with Capsule Networks Artificial Intelligence

Electrocardiogram (ECG) signal analysis plays a vital role in medical diagnosis since ECG signal can provide vital information that can help to diagnose various health conditions. For example, ECG beat classification; e.g classifying ECG signal portions in to classes such as normal beats or different arrhythmia types such as atrial fibrillation, premature contraction, or ventricular fibrillation allows to identify different cardiovascular diseases. Similarly, ECG signal compression and reconstruction have a variety of applications such as remote cardiac monitoring in body sensor nodes (Mamaghanian et al., 2011) and achieving low power consumption when sending and processing data through IoT -gateways (Al Disi et al., 2018). ECG signal analysis and classification was predominantly done using signal processing methods such as wavelet transformation or independent component analysis or feature driven classical machine learning methods (Y u and Chou, 2008; Martis et al., 2013; Kim et al., 2009; Li and Zhou, 2016). However such methods have left room for further improvements in terms of accuracy and the manual feature curation is a daunting task.

Time Series Analysis with Deep Learning : Simplified


Take the crash course in the'whys' and'whens' of using Deep Learning in Time Series Analysis. Time series is a sequence of data points, ordered using time stamps. And time series analysis is.. you guessed it.. analysis of the time series data:P From the daily price of your favorite fruit to the readings of the voltage output provided by a circuit, the scope of time series is huge and so is the field of time series analysis. Analyzing a time series data is usually focused on forecasting, but can also include classification, clustering, anomaly detection etc. For example, by studying the pattern of price variation in the past, you can try forecasting the price of that watch that you have been eyeing for so long, to judge what would be the best time to buy it!!

Time series classification for varying length series Machine Learning

Noname manuscript No. (will be inserted by the editor)Time series classification for varying length series Chang Wei Tan · Fran cois Petitjean· Eamonn Keogh · Geoffrey I. Webb the date of receipt and acceptance should be inserted later Abstract Research into time series classification has tended to focus on the case of series of uniform length. However, it is common for real-world time series data to have unequal lengths. Differing time series lengths may arise from a number of fundamentally different mechanisms. In this work, we identify and evaluate two classes of such mechanisms - variations in sampling rate relative to the relevant signal and variations between the start and end points of one time series relative to one another. We investigate how time series generated by each of these classes of mechanism are best addressed for time series classification. We perform extensive experiments and provide practical recommendations on how variations in length should be handled in time series classification. Keywords Time Series Classification, Proximity Forest, Dynamic Time Warping 1 Introduction Time series classification (TSC) is an important task in many modern world applications such as remote sensing (Pelletier et al., 2019; Petitjean et al., 2012), astronomy (Batista et al., 2011), speech recognition (Hamooni et al., 2016), and insect classification (Chen et al., 2014). The time series to be classified are the observed outputs generated by some process. The classification task often relates to identifying the class of the process that generated the series. Each class of process might be considered as a realization of one or more ideals (in the Platonic sense) or prototypes. The resulting time series can then beChang Wei Tan · Fran cois Petitjean· Geoffrey I. Webb Faculty of Information Technology 25 Exhibition Walk Monash University, Melbourne VIC 3800, Australia Email:,, An observed time series might differ from the ideal in many ways. Much of the research on time series distance measures in the last decade can be seen as the introduction of techniques to mitigate these differences, either as a preprocessing step or directly in a distance measure. For example, variations in amplitude and offset are typically addressed in time series classification by normalization of the series (Rakthanmanon et al., 2012). Some observed values may be erroneous and might be addressed by outlier detection (Basu and Meckesheimer, 2007) and subsequent reinterpolation (Pelletier et al., 2019).

Using Clinical Notes with Time Series Data for ICU Management Machine Learning

Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources. There has been continuous progress in machine learning research for ICU management, and most of this work has focused on using time series signals recorded by ICU instruments. In our work, we show that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management. While the time-series data is measured at regular intervals, doctor notes are charted at irregular times, making it challenging to model them together. We propose a method to model them jointly, achieving considerable improvement across benchmark tasks over baseline time-series model. Our implementation can be found at \url{}.