Time Series Forecasting

The financial market has become one of the first to adopt the machine learning (ML) market. Since the 1980s, people have been using ML to discover the laws of the market.

Other features the implementation requirements plan requires that the special requirements of the partners working together in the supply chain be clearly articulated. Therefore, it is necessary to understand the time series you are considering. Because the characteristics of a sequence are different from the predicted dimensions (time, product, geographic range), different prediction processes must be used. Contingency requirements If there is no demand for a considerable period of time, we call this sequence of events sporadic (intermittent). Those requirements patterns are particularly present in the replacement requirements section or only in a small portion of the forecast demand, such as one day in a store for a particular ...

Every year, the Double 11 is refreshing the world wonder of logistics, but due to big data and collaboration, the task that seems impossible to complete is accelerated every time. Taking a set of data from 2013 to 2016 as an example, from the time of signing, it took 9 days for the double 11 parcel to sign 100 million in 2013, 6 days for 2014, and 4 days for 2015, and 2016 for 2016. The speed increase is only 3.5 days.

Recently, after a deep insight into the development of the domestic electric power industry, IBM first presented and introduced in detail the "smart power" strategy, and proposed a series of solutions to the smart grid construction and new energy access. This is the first time IBM in China to release the "smart Power" strategy and a series of solutions. "Intelligent power" is an important part of IBM's "intelligent City" strategy, which can not only provide powerful impetus for the development of the city, but also contribute to the creation of a better life for the people, this program is the theme of the 2010 Shanghai World Expo ...

Data mining, which mainly solves four kinds of problems, is a very clear definition of several kinds of problems that it can solve. This is a high degree of induction, and the application of data mining is a process to deduce these types of problems. So let's look at how the four types of problems it solves are defined: 1, classification problem classification problem is a predictive problem, but it is different from the general prediction problem, but the difference is that the results of its predictions are categories (such as a, B, C three) rather than a specific value (such as 55, 65, 75 ...). ...)。 ...

Risks, statements, cash, growth rates, expenses ... These are the chief financial officers of the enterprise must pay attention to. Now, with the advent of the "Big Data" era, the 2011 survey by CFO Executive Board, the top agency for best Practice research and quantitative analysis in the US, revealed that the global CFO had to face a series of challenges, and the CFO said the company's 30%~80% Performance potential is lost as a result of substandard enterprise performance management; some CFOs claim that the ratio of losses to 80%;CFO will be 37% of the time spent on performance ...

Most of the domestic discussion of artificial intelligence is fragmentation of fragmentation, it is difficult to understand the development of artificial intelligence and technical system, it is very difficult to have practical reference significance. Deloitte DUP recently released a report detailing the history, core technologies and applications of artificial intelligence, especially the important cognitive techniques. This report will help us to learn more about artificial intelligence and cognitive technology, and help companies in various industries consider the real value of AI applications. This report is translated by the heart of the machine, welcome to the micro-signal: The Heart of the Machine (Id:almo ...)

The purpose of data mining is to find more quality users from the data. Next, we continue to explore the model of the guidance data mining method. What is a guided data mining method model and how data mining builds the model. In building a guided data mining model, the first step is to understand and define the target variables that the model attempts to estimate. A typical case, two-dollar response model, such as selecting a customer model for direct mailing and e-mail marketing campaigns. The build of the model selects historical customer data that responds to similar activities in the past. The purpose of guiding data mining is to find more classes ...

The purpose of data mining is to find more quality users from the data. Next, we continue to explore the model of the guidance data mining method. What is a guided data mining method model and how data mining builds the model. In building a guided data mining model, the first step is to understand and define the target variables that the model attempts to estimate. A typical case, two-dollar response model, such as selecting a customer model for direct mailing and e-mail marketing campaigns. The build of the model selects historical customer data that responds to similar activities in the past. The purpose of guiding data mining is to find more similar ...

The fashion industry, which has been plagued by unpredictable changes in the market, has finally found a good helper.     EDITD and WGSN, a retail technology company, are launching their own large data Analysis Services, which they hope will help fashion brands and retailers predict, and even establish, popular trends.     At the beginning of the 2000, Julia Lia Faller, who also worked in fashion design in Australia, found an annoying problem: there was too little information on hand to help her understand and respond to the latest trends. "We ...