47 skills found · Page 1 of 2
thuml / Nonstationary TransformersCode release for "Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting" (NeurIPS 2022), https://arxiv.org/abs/2205.14415
advaitsave / Introduction To Time Series Forecasting PythonIntroduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.
FilippoMB / Python Time Series HandbookBook and material for the course "Time series analysis with Python" (STA-2003)
Hank0626 / TimeBridgeOfficial implementation of "TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting" (ICML 2025)
Yunbo426 / MIMCode release for "Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics" (CVPR 2019)
GeneSUN / Time Series Analysis ToolkitA comprehensive toolkit for time series analysis, including scripts for visualizing results, detecting stationarity, trends, seasonality, and heteroscedasticity, as well as building models, and evaluating performance
ritchieng / Fractional Differencing GpuRapid large-scale fractional differencing with NVIDIA RAPIDS and GPU to minimize memory loss while making a time series stationary. 6x-400x speed up over CPU implementation.
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
kimanki / TAFASBattling the Non-stationarity in Time Series Forecasting via Test-time Adaptation (AAAI 2025)
adrienpetralia / NILMFormer[KDD 2025] NILMFormer: A Sequence-To-Sequence Non-Stationarity Aware Transformer for Non-Intrusive Load Monitoring
Lekshmi2003-glitch / Time Series Analysis Using R Shiny📈 A Shiny dashboard for interactive time series analysis. Supports CSV/Excel upload, plotting, decomposition, stationarity tests, SARIMA, GARCH, smoothing methods, and PDF report export. Ideal for researchers, analysts, and students.
Pradnya1208 / Bitcoin Price Prediction Using ARIMABitcoin price prediction using ARIMA Model.
viniciuserra / Gearbox Hybrid CNN SVMThe features of nonlinearity and non-stationarity in real systems are often difficult to be extracted. This paper focuses on developing a Convolutional Neural Network (CNN) to obtain features directly from the original vibration signals of a gearbox with different pinion conditions. Experimental data is used to show the efficiency of the presented method. Support Vector Machine (SVM) is utilized to classify feature sets extracted with 1D-CNN. The obtained results show that the features extracted in this method have excellent quality for fault classification without any additional feature selection.
Tobias-Mann / Pairs Trading AnalyzerC# Console Application: Asks for two files containing historical financial data in the same format as files from Yahoo Finance. Performs the two-step Engel-Granger Test for Cointegration and simulates profits of applying the Pairs Trading Strategy to these stocks. To Project further Includes code to conduct statistical inference and a Function to perform the Augmented Dickey-Fuller Test for stationarity of a time series, which is part of the Engel-Granger Test for cointegration.
coolsunxu / MIM PytorchMemory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics
AaltoML / PeriodicBNNCode for 'Periodic Activation Functions Induce Stationarity' (NeurIPS 2021)
mehulsharma3795 / Stock Options Pricing Model• Visualised trend and seasonality & conducted tests for checking stationarity of Time series for predicting volatility using GARCH Model. • Developed Cox-Ross-Rubenstein Binomial Tree Model for pricing American Call & Put Options.• Programatically Implemented Black Sholes Merton Model for pricing European Call & Put Options.
bhattbhavesh91 / Adf Test Stationarity PythonStationarity check using the Augmented Dickey-Fuller test from Scratch in Python
ml-jku / Reactive ExplorationCode for the paper "Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning"
bugsuse / MIM PyTorchPyTorch Code of "Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics"
