Research group at MIT discovered new way of tracking sleep phase. WiFi can interfere, but they used deep learning to clear the signal and to achieve 80% accuracy in sleep stage prediction, compapable with a lab equipment.
http://news.mit.edu/2017/new-ai-algorithm-monitors-sleep-radio-waves-0807
#timeseries #eeg #deep_learning #mit #sleep
http://news.mit.edu/2017/new-ai-algorithm-monitors-sleep-radio-waves-0807
#timeseries #eeg #deep_learning #mit #sleep
MIT News
New AI algorithm monitors sleep with radio waves
Researchers at MIT and Massachusetts General Hospital have devised a new way to monitor sleep without any kind of sensors attached to the body. Their sensor uses low-power radio waves that detect small changes in body movement caused by the patientβs breathingβ¦
Time series basics
Time series β data, with points having timestamps. Some might think that #timeseries are mostly used in algorithmic trading, but they often used in malware detection, network data analysis or any other field, dealing with some flow of time-labeled data. These two resources provide deep and easy #introduction into #TS analysis.
Github: https://github.com/akshaykapoor347/Time-series-modeling-basics
Data Camp presentation: https://s3.amazonaws.com/assets.datacamp.com/production/course_5702/slides/chapter3.pdf
#beginner #novice #python #entrylevel
Time series β data, with points having timestamps. Some might think that #timeseries are mostly used in algorithmic trading, but they often used in malware detection, network data analysis or any other field, dealing with some flow of time-labeled data. These two resources provide deep and easy #introduction into #TS analysis.
Github: https://github.com/akshaykapoor347/Time-series-modeling-basics
Data Camp presentation: https://s3.amazonaws.com/assets.datacamp.com/production/course_5702/slides/chapter3.pdf
#beginner #novice #python #entrylevel
GitHub
GitHub - akshaykapoor347/Time-series-modeling-basics: Basics of Time series modeling in Python using pandas
Basics of Time series modeling in Python using pandas - GitHub - akshaykapoor347/Time-series-modeling-basics: Basics of Time series modeling in Python using pandas
ββHow is Uber predicting demand, surge and where will be high demand area.
One more post from brilliant #Uber engineering team, sharing their approach and general experience about forecasting.
Link: https://eng.uber.com/forecasting-introduction/
#ts #timeseries #arima #demandprediction #ml
One more post from brilliant #Uber engineering team, sharing their approach and general experience about forecasting.
Link: https://eng.uber.com/forecasting-introduction/
#ts #timeseries #arima #demandprediction #ml
AR-Net: A simple autoregressive NN for #timeSeries
AR-Net, has 2 distinct advantages over its traditional counterpart:
* scales well to large orders, making it possible to estimate long-range dependencies (important in high-resolution monitoring applications, such as those in the data center domain);
* automatically selects and estimates the important coefficients of a sparse AR process, eliminating the need to know the true order of the AR process
To overcome the scalability challenge, they train a NN with #SGD to learn the AR (#autoregression) coefficients. AR-Net effectively learns near-identical weights as classic AR implementations & is equally good at predicting the next value of the time series.
Also, AR-Net automatically learns the relevant weights, even if the underlying data is generated by a noisy & extremely sparse AR process.
blog: https://ai.facebook.com/blog/ar-net-a-simple-autoregressive-neural-network-for-time-series/
paper: https://arxiv.org/abs/1911.03118
AR-Net, has 2 distinct advantages over its traditional counterpart:
* scales well to large orders, making it possible to estimate long-range dependencies (important in high-resolution monitoring applications, such as those in the data center domain);
* automatically selects and estimates the important coefficients of a sparse AR process, eliminating the need to know the true order of the AR process
To overcome the scalability challenge, they train a NN with #SGD to learn the AR (#autoregression) coefficients. AR-Net effectively learns near-identical weights as classic AR implementations & is equally good at predicting the next value of the time series.
Also, AR-Net automatically learns the relevant weights, even if the underlying data is generated by a noisy & extremely sparse AR process.
blog: https://ai.facebook.com/blog/ar-net-a-simple-autoregressive-neural-network-for-time-series/
paper: https://arxiv.org/abs/1911.03118
Article on how to use #XGBoost for #timeseries forcasting
Link: https://machinelearningmastery.com/xgboost-for-time-series-forecasting/
Link: https://machinelearningmastery.com/xgboost-for-time-series-forecasting/
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ββOrbit β An Open Source Package for Time Series Inference and Forecasting
Object-ORiented BayesIan Time Series is a new project for #timeseries forecasting by #Uber team. Has #scikit-learn compatible interface and claimed to have results comparable to #prophet .
Post: https://eng.uber.com/orbit/
Docs: https://uber.github.io/orbit/about.html
GitHub: https://github.com/uber/orbit/
Object-ORiented BayesIan Time Series is a new project for #timeseries forecasting by #Uber team. Has #scikit-learn compatible interface and claimed to have results comparable to #prophet .
Post: https://eng.uber.com/orbit/
Docs: https://uber.github.io/orbit/about.html
GitHub: https://github.com/uber/orbit/
ββTSMixer: An All-MLP Architecture for Time Series Forecasting
Time-series datasets in real-world scenarios are inherently multivariate and riddled with intricate dynamics. While recurrent or attention-based deep learning models have been the go-to solution to address these complexities, recent discoveries have shown that even basic univariate linear models can surpass them in performance on standard academic benchmarks. As an extension of this revelation, the paper introduces the Time-Series Mixer TSMixer. This innovative design, crafted by layering multi-layer perceptrons, hinges on mixing operations across both time and feature axes, ensuring an efficient extraction of data nuances.
Upon application, TSMixer has shown promising results. Not only does it hold its ground against specialized state-of-the-art models on well-known benchmarks, but it also trumps leading alternatives in the challenging M5 benchmark, a dataset that mirrors the intricacies of retail realities. The paper's outcomes emphasize the pivotal role of cross-variate and auxiliary data in refining time series forecasting.
Paper link: https://arxiv.org/abs/2303.06053
Code link: https://github.com/google-research/google-research/tree/master/tsmixer
A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-tsmixer
#paperreview #deeplearning #timeseries #mlp
Time-series datasets in real-world scenarios are inherently multivariate and riddled with intricate dynamics. While recurrent or attention-based deep learning models have been the go-to solution to address these complexities, recent discoveries have shown that even basic univariate linear models can surpass them in performance on standard academic benchmarks. As an extension of this revelation, the paper introduces the Time-Series Mixer TSMixer. This innovative design, crafted by layering multi-layer perceptrons, hinges on mixing operations across both time and feature axes, ensuring an efficient extraction of data nuances.
Upon application, TSMixer has shown promising results. Not only does it hold its ground against specialized state-of-the-art models on well-known benchmarks, but it also trumps leading alternatives in the challenging M5 benchmark, a dataset that mirrors the intricacies of retail realities. The paper's outcomes emphasize the pivotal role of cross-variate and auxiliary data in refining time series forecasting.
Paper link: https://arxiv.org/abs/2303.06053
Code link: https://github.com/google-research/google-research/tree/master/tsmixer
A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-tsmixer
#paperreview #deeplearning #timeseries #mlp
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