Time series is a sequence of numerical data points in successive order and time series analysis is the technique of analysing the available data to predict the future outcome of an application. At present, time series analysis has been utilised in a number of applications, including stock market analysis, economic forecasting, pattern recognition, and sales forecasting. Here is a list of top ten Python tools, in no particular order, for Time Series Analysis. About: Arrow is a Python library that offers a human-friendly approach to creating, manipulating, formatting and converting dates, times and timestamps. The library implements and updates the datetime type, plugging gaps in functionality and providing an intelligent module API that supports many common creation scenarios. About: Cesium is an open source library that allows users to extract features from raw time series data, build machine learning models from these features, as well as generate predictions for new data.
This multiple session tutorial is designed to train researchers and practitioners to begin analyzing synchrophasor (i.e., PMU) and point on wave data. The course covers concepts from power engineering and data science, and will show attendees how to develop efficient workflows for analyzing and visualizing time series data at scale. The first day of the course will cover foundational concepts from power systems engineering, and will relate PMU data to physical properties of the grid. The session will discuss phasor calculation, and methods for using phasor data to compute frequency. We will close with a summary of best practices and lessons learned from using PMU data in industry.
Time has always been a crucial factor when we record or collect data. And in time series analysis, time is a vital variable of the data. Time series analysis helps us to study the progress over a period of time. Time Series is a series of observations taken at specific time intervals to determine the trends, forecast the future, and sometimes to perform a few other analyses. The analysis is done on the basis of previously observed values and intervals.
How PostgreSQL accidentally became the ideal platform for IoT applications and services. From mainframes (1950s-1970s), to Personal Computers (1980s-1990s), to smartphones (2000s-now), each wave brought us smaller, yet more powerful machines, that were increasingly plentiful and pervasive throughout business and society. We are now sitting on the cusp of another inflection point, or major release if you will, with computing so small and so common that it is becoming nearly as pervading as the air we breathe. With each wave, software developers and businesses initially struggle to identify the appropriate software infrastructure on which to develop their applications. But soon common platforms emerge: Unix; Windows; the LAMP stack; iOS/Android.
Time-series analysis has been studied for more than a hundred years, however, the extraordinary growth of data available from numerous sources and more frequent growth of data alongside the growth of computer power (GPU & Multicore) makes the analysis of large-scale time-series data possible today in a way that was not previously practical. The use of time-series data has been traditionally linked to sectors where time is not just a metric but a primary axis, such as in finance, Industrial IoT, and energy. However, in the last 10 years, it is starting to be generally used in other sectors such as marketing, gambling, or any other sector where performance monitoring and time-series analysis is needed. There are three main solutions in the ecosystem to treat, analyze, and visualize time-series data. These are Time-series Databases, Time-Series Data Analytics Solutions, and Machine Learning Platforms.
This article aims to introduce some standard techniques used in time-series analysis and walks through the iterative steps required to manipulate and visualize time-series data. Maruti Suzuki India Limited, formerly known as Maruti Udyog Limited, is an automobile manufacturer in India. It is a 56.21% owned subsidiary of the Japanese car and motorcycle manufacturer Suzuki Motor Corporation. Fire up the editor of your choice and type in the following code to import the required libraries and data. The data has been taken from Kaggle.
A time series data is a set of observation on the value that a variable takes of different time, such data may be collected at regular time intervals such as daily stock price, monthly money supply figures, annual GDP etc. Time series data have a natural temporal ordering. This makes time series analysis distinct from other common data analysis problems in which there is no natural order of the observation. In simple word we can say, the data which are collected in according to time is called time series data. On the other hand, the data which are collected by observing many subject at the same point of time is called cross sectional data. A time series is a set of observations meas ured at time or space intervals arranged in chrono logical order.
Data augmentation methods have been shown to be a fundamental technique to improve generalization in tasks such as image, text and audio classification. Recently, automated augmentation methods have led to further improvements on image classification and object detection leading to state-of-the-art performances. Nevertheless, little work has been done on time-series data, an area that could greatly benefit from automated data augmentation given the usually limited size of the datasets. We present two sample-adaptive automatic weighting schemes for data augmentation: the first learns to weight the contribution of the augmented samples to the loss, and the second method selects a subset of transformations based on the ranking of the predicted training loss. We validate our proposed methods on a large, noisy financial dataset and on time-series datasets from the UCR archive. On the financial dataset, we show that the methods in combination with a trading strategy lead to improvements in annualized returns of over 50$\%$, and on the time-series data we outperform state-of-the-art models on over half of the datasets, and achieve similar performance in accuracy on the others.
Granger causal modeling is an emerging topic that can uncover Granger causal relationship behind multivariate time series data. In many real-world systems, it is common to encounter a large amount of multivariate time series data collected from different individuals with sharing commonalities. However, there are ongoing concerns regarding Granger causality's applicability in such large scale complex scenarios, presenting both challenges and opportunities for Granger causal structure reconstruction. Existing methods usually train a distinct model for each individual, suffering from inefficiency and over-fitting issues. To bridge this gap, we propose an Inductive GRanger cAusal modeling (InGRA) framework for inductive Granger causality learning and common causal structure detection on multivariate time series, which exploits the shared commonalities underlying the different individuals. In particular, we train one global model for individuals with different Granger causal structures through a novel attention mechanism, called prototypical Granger causal attention. The model can detect common causal structures for different individuals and infer Granger causal structures for newly arrived individuals. Extensive experiments, as well as an online A/B test on an E-commercial advertising platform, demonstrate the superior performances of InGRA.
Time series data is a collection of chronological observations which is generated by several domains such as medical and financial fields. Over the years, different tasks such as classification, forecasting, and clustering have been proposed to analyze this type of data. Time series data has been also used to study the effect of interventions over time. Moreover, in many fields of science, learning the causal structure of dynamic systems and time series data is considered an interesting task which plays an important role in scientific discoveries. Estimating the effect of an intervention and identifying the causal relations from the data can be performed via causal inference. Existing surveys on time series discuss traditional tasks such as classification and forecasting or explain the details of the approaches proposed to solve a specific task. In this paper, we focus on two causal inference tasks, i.e., treatment effect estimation and causal discovery for time series data, and provide a comprehensive review of the approaches in each task. Furthermore, we curate a list of commonly used evaluation metrics and datasets for each task and provide in-depth insight. These metrics and datasets can serve as benchmarks for research in the field.