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Introduction to Forecasting in Machine Learning and Deep Learning
Forecasts are critical in many fields, including finance, manufacturing, and meteorology. At Uber, probabilistic time series forecasting is essential for marketplace optimization, accurate hardware capacity predictions, marketing spend allocations, and real-time system outage detection across millions of metrics.
In this talk, Franziska Bell provides an overview of classical, machine learning and deep learning forecasting approaches. In addition fundamental forecasting best practices will be covered. This video was recorded at QCon.ai 2018: http://bit.ly/2piRtLl If you are a software engineer that wants to learn more about machine learning check our dedicated introductory guide http://bit.ly/2HPyuzY . For more awesome presentations on innovator and early adopter, topics check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz
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Predicting with a Neural Network explained
In this video, we explain the concept of using an artificial neural network to predict on new data. We also show how to predict in code with Keras.
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Time Series Forecasting
Time Series Forecasting - Statistical
Classical time series forecasting methods may be focused on linear relationships, nevertheless, they are sophisticated and perform well on a wide range of problems, assuming that your data is suitably prepared and the method is well configured. 11 Classical Time Series Forecasting Methods in Python (Cheat Sheet) | Jason Brownlee - Machine Learning Mastery
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Modern Time Series Analysis | SciPy 2019 Tutorial | Aileen Nielsen
This tutorial will cover the newest and most successful methods of time series analysis. 1. Bayesian methods for time series 2. Adapting common machine learning methods for time series 3. Deep learning for time series These methods are producing state-of-the-art results in a variety of disciplines, and attendees will learn both the underlying concepts and the Python implementations and uses of these analytical approaches to generate forecasts and estimate uncertainty for a variety of scientific time series.
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Autoregression (AR)
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Moving Average (MA)
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Autoregressive Moving Average (ARMA)
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Autoregressive Integrated Moving Average (ARIMA)
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Seasonal Autoregressive Integrated Moving-Average (SARIMA)
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Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors (SARIMAX)
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Vector Autoregression (VAR)
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Volume Weighted Moving Average (VWMA)
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Vector Autoregression Moving-Average (VARMA)
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Vector Autoregression Moving-Average with Exogenous Regressors (VARMAX)
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Smoothing
Simple Exponential Smoothing (SES)
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Holt's Exponential Smoothing
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Winter's (Holt-Winter's) Exponential Smoothing (HWES)
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Time Series AutoML
Time Series Forecasting - Deep Learning
Applying deep learning methods like Multilayer Neural Networks and Long Short-Term Memory (LSTM) Recurrent Neural Network models to time series forecasting problems.| Jason Brownlee - Machine Learning Mastery
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Forecasting with Neural Networks: Part A
Galit Shmueli What is a neural network, neural network terminology, and setting up a network for time series forecasting This video supports the textbook Practical Time Series Forecasting. http://www.forecastingbook.com http://www.galitshmueli.com
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Dafne van Kuppevelt | Deep learning for time series made easy
PyData Amsterdam 2017 Deep learning is a state of the art method for many tasks, such as image classification and object detection. For researchers that have time series data, but are not an expert on deep learning, the barrier can be high to start using deep learning. We developed mcfly, an open source python library, to help machine learning novices explore the value of deep learning for time series data. In this talk, we will explore how machine learning novices can be aided in the use of deep learning for time series classification. In a variety of scientific fields researchers face the challenge of time series classification. For example, to classify activity types from wrist-worn accelerometer data or to classify epilepsy from electroencephalogram (EEG) data. For researchers who are new to the field of deep learning, the barrier can be high to start using deep learning. In contrast to computer vision use cases, where there are tools such as caffe that provide pre-defined models to apply on new data, it takes some knowledge to choose an architecture and hyperparameters for the model when working with time series data. We developed mcfly, an open source python library to make time series classification with deep learning easy. It is a wrapper around Keras, a popular Python library for deep learning. Mcfly provides a set of suitable architectures to start with, and performs a search over possible hyper-parameters to propose a most suitable model for the classification task provided. We will demonstrate mcfly with excerpts from (multi-channel) time series data from movement sensors that are associated with a class label, namely activity type (sleeping, walking, climbing stairs). In our example, mcfly will be used to train a deep learning model to label new data. Code for This Video Course Homepage
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Time Series Data Encoding for Deep Learning, TensorFlow and Keras (10.1)
Time series data is usually represented in the form of sequences when working with Keras and TensorFlow. In this video sequences are introduced for time series prediction.
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Jeffrey Yau: Time Series Forecasting using Statistical and Machine Learning Models | PyData NYC 2017
PyData New York City 2017 Time series data is ubiquitous, and time series modeling techniques are data scientists’ essential tools. This presentation compares Vector Autoregressive (VAR) model, which is one of the most important class of multivariate time series statistical models, and neural network-based techniques, which has received a lot of attention in the data science community in the past few years.
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Joe Jevnik - A Worked Example of Using Neural Networks for Time Series Prediction
PyData New York City 2017 Slides: https://github.com/llllllllll/osu-talk Most neural network examples and tutorials use fake data or present poorly performing models. In this talk, we will walk through the process of implementing a real model, starting from the beginning with data collection and cleaning. We will cover topics like feature selection, window normalization, and feature scaling. We will also present development tips for testing and deploying models.
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Demand Forecasting
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Demand Forecasting Using AI and Machine Learning (AI For Business Episode 1)
In this video, we will explore how Machine Learning is used for demand forecasting. Enjoy the video and please like, subscribe and turn on the notifications. In this video, I will start with identifying the challenge, followed by exploring the solution, then providing background of the process and the technology behind it, and finally how it can be applied in the enterprise together with some examples.
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Samsung Cello Demand Sensing: An AI-enabled demand forecasting tool
By creating weekly sellout forecasting based on Cello Supply Chain Management (SCM) platform technologies and Samsung SDS' own big data analytics engine Brightics AI, Cello Demand Sensing helps you forecast demand accurately. Find out more at https://www.CelloLogistics.com Subscribe to our channel: https://www.youtube.com/c/cellologistics
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