If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated. Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. What kind of problems does deep learning solve, and more importantly, can it solve yours?
Python is often the language of choice for developers who need to apply statistical techniques or data analysis in their work. It is also used by data scientists whose tasks need to be integrated with web apps or production environments. Its combination of consistent syntax, shorter development time and flexibility makes it well-suited to developing sophisticated models and prediction engines that can plug directly into production systems. One of Python's greatest assets is its extensive set of libraries. Libraries are sets of routines and functions that are written in a given language.
Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available. This data represents a multivariate time series of power-related variables, that in turn could be used to model and even forecast future electricity consumption. In this tutorial, you will discover a household power consumption dataset for multi-step time series forecasting and how to better understand the raw data using exploratory analysis. How to Load and Explore Household Electricity Usage Data Photo by Sheila Sund, some rights reserved. The Household Power Consumption dataset is a multivariate time series dataset that describes the electricity consumption for a single household over four years.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. Time series are widely used for non-stationary data, like economic, weather, stock price, and retail sales in this post. We will demonstrate different approaches for forecasting retail sales time series. We are using Superstore sales data that can be downloaded from here.
Small computers, such as Arduino devices, can be used within buildings to record environmental variables from which simple and useful properties can be predicted. One example is predicting whether a room or rooms are occupied based on environmental measures such as temperature, humidity, and related measures. This is a type of common time series classification problem called room occupancy classification. In this tutorial, you will discover a standard multivariate time series classification problem for predicting room occupancy using the measurements of environmental variables. A standard time series classification data set is the "Occupancy Detection" problem available on the UCI Machine Learning repository.
But I'll give you a quick refresher of what a univariate time series is, before going into the details of a multivariate time series. Let's look at them one by one to understand the difference. A univariate time series, as the name suggests, is a series with a single time-dependent variable. For example, have a look at the sample dataset below that consists of the temperature values (each hour), for the past 2 years. Here, temperature is the dependent variable (dependent on Time).
Google does not always get things right, or get to things first. But when Google sets its sights on something, you know that something is about to attract interest. With Google having just announced its Cloud Inference API to uncover insights from time series data, it's a good time to check the options for processing time series data. A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time.
Spatio-temporal (ST) data, which represent multiple time series data corresponding to different spatial locations, are ubiquitous in real-world dynamic systems, such as air quality readings. Forecasting over ST data is of great importance but challenging as it is affected by many complex factors, including spatial characteristics, temporal characteristics and the intrinsic causality between them. In this paper, we propose a general framework (HyperST-Net) based on hypernetworks for deep ST models. More specifically, it consists of three major modules: a spatial module, a temporal module and a deduction module. Among them, the deduction module derives the parameter weights of the temporal module from the spatial characteristics, which are extracted by the spatial module. Then, we design a general form of HyperST layer as well as different forms for several basic layers in neural networks, including the dense layer (HyperST-Dense) and the convolutional layer (HyperST-Conv). Experiments on three types of real-world tasks demonstrate that the predictive models integrated with our framework achieve significant improvements, and outperform the state-of-the-art baselines as well.