Theory and Algorithms for Forecasting Non-Stationary Time Series (NIPS 2016 tutorial)
Vitaly Kuznetsov, Mehryar Mohri
Time series appear in a variety of key real-world applications such as signal processing, including audio and video processing; the analysis of natural phenomena such as local weather, global temperature, and earthquakes; the study of economic variables such as stock values, sales amounts, energy demand; and many other areas. But, while time series forecasting is critical for many applications, it has received little attention in the ML community in recent years, probably due to a lack of familiarity with time series and the fact that standard i.i.d. learning concepts and tools are not readily applicable in that scenario.
This tutorial precisely addresses these and many other related questions. It provides theoretical and algorithmic tools for research related to time series and for designing new solutions. We first present a concise introduction to time series, including basic concepts, common challenges and standard models. Next, we discuss important statistical learning tools and results developed in recent years and show how they are useful for deriving guarantees and designing algorithms both in stationary and non-stationary scenarios. Finally, we show how the online learning framework can be leveraged to derive algorithms that tackle important and notoriously difficult problems including model selection and ensemble methods.
Видео Theory and Algorithms for Forecasting Non-Stationary Time Series (NIPS 2016 tutorial) канала Steven Van Vaerenbergh
Time series appear in a variety of key real-world applications such as signal processing, including audio and video processing; the analysis of natural phenomena such as local weather, global temperature, and earthquakes; the study of economic variables such as stock values, sales amounts, energy demand; and many other areas. But, while time series forecasting is critical for many applications, it has received little attention in the ML community in recent years, probably due to a lack of familiarity with time series and the fact that standard i.i.d. learning concepts and tools are not readily applicable in that scenario.
This tutorial precisely addresses these and many other related questions. It provides theoretical and algorithmic tools for research related to time series and for designing new solutions. We first present a concise introduction to time series, including basic concepts, common challenges and standard models. Next, we discuss important statistical learning tools and results developed in recent years and show how they are useful for deriving guarantees and designing algorithms both in stationary and non-stationary scenarios. Finally, we show how the online learning framework can be leveraged to derive algorithms that tackle important and notoriously difficult problems including model selection and ensemble methods.
Видео Theory and Algorithms for Forecasting Non-Stationary Time Series (NIPS 2016 tutorial) канала Steven Van Vaerenbergh
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
![Modern Time Series Analysis | SciPy 2019 Tutorial | Aileen Nielsen](https://i.ytimg.com/vi/v5ijNXvlC5A/default.jpg)
![Lab 4 2 Non Stationary Data and the Dickey Fuller Test](https://i.ytimg.com/vi/6jgbBqxVYrU/default.jpg)
![Ian Goodfellow: Adversarial Machine Learning (ICLR 2019 invited talk)](https://i.ytimg.com/vi/sucqskXRkss/default.jpg)
![How do I select features for Machine Learning?](https://i.ytimg.com/vi/YaKMeAlHgqQ/default.jpg)
![162 - An introduction to time series forecasting - Part 2 Exploring data using python](https://i.ytimg.com/vi/tnaq2Ao4KBE/default.jpg)
![J. Frankle & M. Carbin: The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://i.ytimg.com/vi/s7DqRZVvRiQ/default.jpg)
![Econometrics - Stationarity in time series data](https://i.ytimg.com/vi/utHNWsBIGTw/default.jpg)
![Denoising Data with FFT [Python]](https://i.ytimg.com/vi/s2K1JfNR7Sc/default.jpg)
![J. Z. Kolter and A. Madry: Adversarial Robustness - Theory and Practice (NeurIPS 2018 Tutorial)](https://i.ytimg.com/vi/TwP-gKBQyic/default.jpg)
![ECL8202, Cours 11.3 Modèles ARIMA pour les séries temporelles](https://i.ytimg.com/vi/lVJxQKRGIsU/default.jpg)
![Detrending a Time Series | Linear and Quadratic Detrending | Financial Time Series Analysis](https://i.ytimg.com/vi/aYiUjaxeghk/default.jpg)
![Introduction to Forecasting in Machine Learning and Deep Learning](https://i.ytimg.com/vi/bn8rVBuIcFg/default.jpg)
!["Least Square Method " In Time Series from Statistics Subject](https://i.ytimg.com/vi/Rfl5cxnf1UI/default.jpg)
![Exponential Smoothing Theory | Forecasting | Time Series](https://i.ytimg.com/vi/ZX706k_ZMzA/default.jpg)
![Time Series Forecasting Using Recurrent Neural Network and Vector Autoregressive Model: When and How](https://i.ytimg.com/vi/i40Road82No/default.jpg)
![Anomalies, Breaks, and Outliers Detection in Time Series](https://i.ytimg.com/vi/h_fLb6YU87c/default.jpg)
![Lecture 10. Time series forecasting with Multiple Linear Regression](https://i.ytimg.com/vi/td9QLuijh9c/default.jpg)
![Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka](https://i.ytimg.com/vi/e8Yw4alG16Q/default.jpg)
![How to fit a SARIMA Model on time series data](https://i.ytimg.com/vi/z-uSBE8Pxwg/default.jpg)
![Forecasting Future Sales Using ARIMA and SARIMAX](https://i.ytimg.com/vi/2XGSIlgUBDI/default.jpg)