This video provides a brief tutorial of using Times Series tools on historical single family home sales and includes an overview on how to configure the following tools:
Field Summary, ARIMA, ETS, TS Compare, and TS Forecast
Data for this training can be downloaded at: http://downloads.alteryx.com/Product-Training/OnDemand/Basic/time_series_data.zip
Time Series is comprised of a variety of tools within Alteryx, which are part of the standard Alteryx Designer License. Video Rating: / 5
This lecture provides an overview of Time Series forecasting techniques and the process of creating effective forecasts. We will go through some of the popular statistical methods including time series decomposition, exponential smoothing, Holt-Winters, ARIMA, and GLM Models. These topics will be discussed in detail and we will go through the calibration and diagnostics effective time series models on a number of diverse datasets. Video Rating: / 5
This video shows the usage and performance for the genetic algo. S&P500 Index daily chart with RSI and cycles combination. GA evolves possible trading systems.
This video shows how to build, train and deploy a time series forecasting solution with Azure Machine Learning. You are guided through every step of the modeling process including:
• Set up your development environment
• Access and examine the data
• Train using an Automated Machine Learning
• Explore the results
• Register and access your time series forecasting model through the Azure portal. Video Rating: / 5
Прогноз продаж сети магазинов Walmart используя исторические данные и регрессионный анализ Microsoft Azure Machine Learning Studio Video Rating: / 5
The General Data Protection Regulation (GDPR), which came into effect on May 25, 2018, establishes strict guidelines for managing personal and sensitive data, backed by stiff penalties. GDPR’s requirements have forced some companies to shut down services and others to flee the EU market altogether. GDPR’s goal to give consumers control over their data and, thus, increase consumer trust in the digital ecosystem is laudable. However, there is a growing feeling that GDPR has dampened innovation in machine learning & AI applied to personal and/or sensitive data. After all, ML & AI are hungry for rich, detailed data and sanitizing data to improve privacy typically involves redacting or fuzzing inputs, which multiple studies have shown can seriously affect model quality and predictive power. While this is technically true for some privacy-safe modeling techniques, it’s not true in general. The root cause of the problem is two-fold. First, most data scientists have never learned how to produce great models with great privacy. Second, most companies lack the systems to make privacy-safe machine learning & AI easy. This talk will challenge the implicit assumption that more privacy means worse predictions. Using practical examples from production environments involving personal and sensitive data, the speakers will introduce a wide range of techniques–from simple hashing to advanced embeddings–for high-accuracy, privacy-safe model development. Key topics include pseudonymous ID generation, semantic scrubbing, structure-preserving data fuzzing, task-specific vs. task-independent sanitization and ensuring downstream privacy in multi-party collaborations. Special attention will be given to Spark-based production environments.
Talk by Jeffrey Yau. Video Rating: / 5
Taking a look at seasonal data (Sunspots) and creating a function that can be used to predict values in the future.
(Recorded with http://screencast-o-matic.com) Video Rating: / 5
One day ahead electricity load forecasting in Matlab with the help of the Artificial neural network.
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The efficient-market hypothesis posits that stock prices are a function of information and rational expectations, and that newly revealed information about a company’s prospects is almost immediately reflected in the current stock price. This would imply that all publicly known information about a company, which obviously includes its price history, would already be reflected in the current price of the stock. Accordingly, changes in the stock price reflect release of new information, changes in the market generally, or random movements around the value that reflects the existing information set. Burton Malkiel, in his influential 1973 work A Random Walk Down Wall Street, claimed that stock prices could therefore not be accurately predicted by looking at price history. As a result, Malkiel argued, stock prices are best described by a statistical process called a „random walk“ meaning each day’s deviations from the central value are random and unpredictable. This led Malkiel to conclude that paying financial services persons to predict the market actually hurt, rather than helped, net portfolio return. A number of empirical tests support the notion that the theory applies generally, as most portfolios managed by professional stock predictors do not outperform the market average return after accounting for the managers‘ fees.
While the efficient-market hypothesis finds favor among financial academics, but its critics point to instances in which actual market experience differs from the prediction-of-unpredictability the hypothesis implies. A large industry has grown up around the implication proposition that some analysts can predict stocks better than others; ironically that would be impossible under the Efficient Markets Hypothesis if the stock prediction industry did not offer something its customers believed to be of value.