I myself do epidemiologic research, which rarely calls for developing machine learning models. Instead, I spend my time developing logistic regression models that I have to be able to interpret for the broader scientific (and sometimes non-scientific) community. I have to be able to explain in some easy, risk-communication way not only the… Continue
Added by Monika Wahi on December 17, 2020 at 7:47am —
It is always hard to find a proper model to forecast time series data. One of the reasons is that models that use time-series data often expose to serial correlation. In this article, we will compare k nearest neighbor (KNN) regression which is a supervised machine learning method, with a more classical and stochastic process, autoregressive integrated moving average (ARIMA).
We will use the monthly prices of refined gold futures(XAUTRY) for one gram… Continue
Added by Selcuk Disci on September 30, 2020 at 10:00am —
This article is chunk from one of my blog posts on Arima time series forecasting with Python It is a pretty extensive tutorial and until and unless you are not really interested in learning in and outs of about ARIMA time series forecasting don't bother to click.
But I do wanted to share this list of 5 very useful metrics for a… Continue
Added by Mohit Sharma on June 1, 2020 at 8:30pm —
Summary: Workforce forecasting and scheduling applications are rapidly upgrading their use of AI. Techniques of time series forecasting ranging from the simple Holt Winters to the complex, DNNs and Multiple Temporal Aggregation are available on some but not all platforms. Increasingly, AI differentiates the usefulness of these apps.
Added by William Vorhies on January 28, 2020 at 2:15pm —
New home construction plays a significant role in housing economy, while simultaneously impacting other sectors such as timber, furniture and home appliances. New house sales is also an important indicator of country’s overall economic health and direction. In the last 50 years there has been few significant bumps and turning points in this sector that shaped the trajectory of the overall economy. Here I review the… Continue
Added by Mab Alam on January 25, 2019 at 8:15pm —
In this tutorial a short introduction to Time Series Modeling and Forecasting is presented. Time Series appears in many industries today that rely on predicting and balancing demand and Supply (e-commerce, retailer , ride-sharing, etc..)
Hence, a good understanding of the underlying model generating the data can significantly help in predicting future… Continue
Added by M Baddar on July 22, 2018 at 12:24am —
A time series is a sequence of data points recorded at specific time points - most often in regular time intervals (seconds, hours, days, months etc.). Every organization generates a high volume of data every single day – be it sales figure, revenue, traffic, or operating cost. Time series data mining can generate valuable information for long-term business decisions, yet they are underutilized in most organizations. Below is a list of few possible ways to… Continue
Added by Mab Alam on May 27, 2018 at 9:00pm —
When trend and seasonality is present in a time series, instead of decomposing it manually to fit an ARMA model using the Box Jenkins method, another very popular method is to use the seasonal autoregressive integrated moving average (SARIMA) model which is a generalization of an ARMA model. SARIMA models are denoted SARIMA(p,d,q)(P,D,Q)[S], where S refers to the number of periods in each season, d is the degree of differencing (the number of times the…
Added by Kostas Hatalis on April 12, 2018 at 10:30am —
The majority of industry and academic numeric predictive projects deal with deterministic or point forecasts of expected values of a random variable given some conditional information. In some cases, these predictions are enough for decision making. However, these predictions don’t say much about the uncertainty of your underlying stochastic process. A common desire of all data scientists is to make predictions for an uncertain future. Clearly then, forecasts should… Continue
Added by Kostas Hatalis on March 15, 2018 at 12:00pm —
Sales prediction is an important part of modern business intelligence. First approaches one can apply to predict sales time series are such conventional methods of forecasting as ARIMA and Holt-Winters. But there are several challenges while using these methods. They are: multilevel daily/weekly/monthly/yearly seasonality, many exogenous factors which impact sales, complex trends in different time periods. In such cases, it is not easy to apply conventional methods. Of course, there is… Continue
Added by Bohdan Pavlyshenko on March 8, 2018 at 9:00am —
Time series forecasting is hardly a new problem in data science and statistics. The term is self-explanatory and has been on business analysts’ agenda for decades now: The very first practices of time series analysis and forecasting trace back to the early 1920s.
The underlying idea of time series forecasting is to look at historical data from the time perspective, define the patterns, and yield short or long-term predictions on how – considering the captured patterns – target… Continue
Added by Olexander Kolisnykov on February 14, 2018 at 1:58am —
The “three wise men” in the story of the nativity are believed to be Magi or Zoroastrians. As the story goes, these three wise men followed the Star of Bethlehem in search for the messiah. For the most part, the Zoroastrians supported the one-god concept - and they also believed in a messiah. It made sense for them - sometimes regarded as the original astrologers - to use their special talents to seek out the baby Jesus. It makes sense purely from a narrative standpoint; although to me… Continue
Added by Don Philip Faithful on November 26, 2017 at 8:00am —
One of the main goals in the Bitcoin analytics is price forecasting. There are many factors which influence the price dynamics. The most important factors are: the interaction between supply and demand, attractiveness for investors, financial and macroeconomics indicators, technical indicators such as difficulty, how many blocks were created recently, etc. A very important impact on the cryptocurrency price has trends…
Added by Bohdan Pavlyshenko on October 26, 2017 at 11:30pm —
In this post, we consider different approaches for time series modeling. The forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine learning algorithm are described. Results of different model combinations are shown. For probabilistic modeling the approaches using copulas and Bayesian inference are considered.
Time series analysis, especially forecasting, is an important problem of modern… Continue
Added by Bohdan Pavlyshenko on February 26, 2017 at 5:30am —
It’s 5:05pm EST. Bob, CFO of ABC Inc is about to get on an earnings call after just reporting a 20% miss on earnings due to slower revenue growth than forecasted. Company ABC’s stock price is plummeting, down 25% in extended hour trading. The board is furious and investors demand answers on the discrepancies.
Inaccurate revenue forecast remains one of… Continue
Added by Winnie Cheng on April 4, 2016 at 10:43am —
Shifting market conditions and increasing competition continue to hit pressure points for retailers as a whole. Grocery is no exception. The age of omni-channel is well underway providing more options for consumers. New store formats seem to pop up on a regular basis. Shopper loyalty hangs in the balance and all signs point to its continued erosion. Intercontinental competition is about to escalate.
One approach to overcome some of these challenges is…
Added by Tony Agresta on December 11, 2015 at 6:03am —
In a recent interview on WGN, Bob Mariano, the CEO of Roundy’s was asked the question “What makes a great grocery store?” His response focused on customer care: “A great grocery store is made up of great people that care about their customers and go out of their way to make them feel appreciated.” Today, Mariano’s competes against global companies like Trader Joes and others and plans to merge with The Kroger Company in late 2015. But they still regard themselves as… Continue
Added by Tony Agresta on December 7, 2015 at 7:30am —
In this post, I will cover in-depth a Big Data use case: monitoring and forecasting air pollution.
A typical Big Data use case in the modern Enterprise includes the collection and storage of sensor data, executing data analytics at scale, generating forecasts, creating visualization portals, and automatically raising alerts in the case of abnormal deviations or threshold breaches.
This article will focus on an implemented use case: monitoring and analyzing air quality… Continue
Added by Axibase Corp on June 2, 2015 at 6:00am —