43 skills found · Page 1 of 2
WenjieDu / PyPOTSA Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/classification/clustering/forecasting/anomaly detection/cleaning on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values
petrobras / 3WTimely detections for more proactive and effective actions in offshore oil wells!
DynamicsAndNeuralSystems / PyspiComparative analysis of pairwise interactions in multivariate time series.
AbdullahO / MSSAMultivariate Singular Spectrum Analysis (mSSA): Forecasting and Imputation algorithm for multivariate time series
ajayarunachalam / MsdaLibrary for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector
adlnlp / StockEmotionsRepository for "StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series" accepted by AAAI 2023 Bridge (AI for Financial Services).
hanxiao0607 / AERCAAERCA: Root Cause Analysis of Anomalies in Multivariate Time Series through Granger Causal Discovery (ICLR 2025 Oral)
atsa-es / AtsarApplied time series analysis in R with Stan. Allows fast Bayesian fitting of multivariate time-series models.
DavideNardone / MTSS Multivariate Time Series SoftwareA GP-GPU/CPU Dynamic Time Warping (DTW) implementation for the analysis of Multivariate Time Series (MTS).
yitaohu88 / Empirical Method In FinanceWinter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series. Topic 1: Time series properties of stock market returns and prices Class intro: Forecasting and Finance The random walk hypothesis Stationarity Time-varying volatility and General Least Squares Robust standard errors and OLS Topic 2: Time-dependence and predictability ARMA models The likelihood function, exact and conditional likelihood estimation Predictive regressions, autocorrelation robust standard errors The Campbell-Shiller decomposition Present value restrictions Multivariate analysis: Vector Autoregression (VAR) models, the Kalman Filter Topic 3: Heteroscedasticity Time-varying volatility in the data Realized Variance ARCH and GARCH models, application to Value-at-Risk Topic 4: Time series properties of the cross-section of stock returns Single- and multifactor models Economic factors: Models and data exploration Statistical factors: Principal Components Analysis Fama-MacBeth regressions and characteristics-based factors
matthewswogger / Surge Forecast With RNNMultivariate Time-Series Analysis using Recurrent Neural Net with Long Short Term Memory for forecasting surge pricing 5 min into the future.
husnejahan / Multivariate Time Series Analysis Using LSTM ARIMAMultivariate Time series Analysis Using LSTM & ARIMA
Thinklab-SJTU / UP2MEOfficial implementation of our ICML 2024 paper "UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis"
ljj-cyber / TopoGDNCode repository of “Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis”
Wallot / MdRQAThis repository contains additional resources to the article "Multidimensional Recurrence Quantification Analysis (MdRQA) for the analysis of multidimensional time-series: A software implementation in MATLAB and its application to group-level data in joint action", Frontiers in Psychology, by Wallot, Roepstorff & Mønster, as well as to the article "Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) – A Tutorial in R" by Wallot & Leonardi
Ilyushin / EconomicIntelligenceThe project focused on the use of public data to assess the economic situation in the country based on the state of the stock market and national means of payment, in particular - of the national currency. As sources are used: Open data Ministry of Finance of the Russian Federation These Moscow Exchange Google Finance Data Technologies used: Backend: Databases (relational) - Microsoft SQL Server 2014 Databases (multivariate) models DataMining, OLAP-cube - Microsoft Analysis Services 12.0 Веб-сервер - Windows Server 2012 / Internet Information Services Самописный ASP.NET HTTP Restful интерфейс для взаимодействия с Frontend ETL (загрузка и пре-процессинг данных, управление обновлением данных) SQL Server Integration Services 2014 (разработка в Visual Studio 2013, SSDT) Frontend: AngularJS ChartJS Twitter Bootstrap These were chosen so that the detail (granularity) in the set is not less than 1 day. The result has been created and filled with data analytic repository (Kimball model, topology - star), which was used to build a multi-dimensional databases and OLAP-based cubes on it, as well as models of analysis of data on two main algorithms: Microsoft Time Series, Microsoft Neural Network . To ensure interoperability frontend and backend server for backend-server was set up HTTP-Restful interface JSON-issuing documents in the form of finished sets. The project includes two main areas: Intelligent visualization of open data Analysis of open data and the construction of forecasts based on them Intelligent visualization involves the use of MDX-queries to the OLAP-cube, followed by depression (drilldown) in the data, the system allows the user to quickly find the "weak points" of the economy, as part of the data collected. To predict the time a standard mix of algorithms ARTXP / ARIMA, without the use of queries involving cross-prediction (but it is possible to enroll in the system correct data). These algorithms have been tested primarily on foreign exchange rates (US dollar) and the assets of banks included in the special list of Ministry of Finance. In addition, for assets shows the different customization options algorithms - a long-term, short-term and medium-term (balanced) plan. Assessing the impact of oil prices and foreign currency exchange rate for the total market capitalization was conducted on a sample of the data collected: companies with a total market capitalization of 100 to 500 million rubles, present in the market during 2013-2015 Analytical server builds the neural network receiving the input exchange rates, companies, the weighted average share price, total capitalization of the company and the price of oil to requests received models give the opportunity to evaluate the growth rate of \ fall (if at all) the company's capitalization at historical exchange rates and / or the cost of oil. Built a system can expand to include new indicators, which will significantly increase the accuracy of forecasting.
sinanw / Lstm Stock Price PredictionThis project implements a time series multivariate analysis using RNN/LSTM for stock price predictions. A deep RNN model was created and trained on five years of historical Google stock price data to forecast the stock performance over a two-month period.
IDA-HumanCapital / FifeFinite-Interval Forecasting Engine: Machine learning models for discrete-time survival analysis and multivariate time series forecasting
clisztian / IMetricaA fast, interactive, graphical-user-interface oriented software suite for predictive modeling, multivariate time series analysis, real-time signal extraction, Bayesian financial econometrics, and much more.
xxl4tomxu98 / Vector Autoregressive Model Wage InflationsAn econometrics vector autoregression model (VAR) for analysis of multivariate time series of macroeconomics phenomena. Python Jupyter notebook based model is presented here although other packages like R statistical programming language with R Studio could also be used.
