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If You Want to Beat the Competition, Ask More Interesting Questions

Over the past few months, as I have spoken with Qlik customers, I have been amazed by the clever solutions that have been proposed to sticky business problems. I have seen an app that steers trucks away from inclement weather and another that identifies pre-diabetic patients to trigger alternative care management. These apps deliver profound value to the organizations that developed them, and, usually, they dramatically drive down cost.

AI Experience Roadshow Europe 2019: Highlights from Stockholm, Paris, and Madrid

This article is by James Lawson and originally appeared on the DataRobot Blog here: https://blog.datarobot.com/ai-experience-roadshow-europe-2019-highlights-from-stockholm-paris-and-madrid

 

Following the success of AI Experience London earlier in the year and in response to customer demand, we decided to extend the roadshow to other European cities.

Expand Your Predictive Palette III.I: Sales Forecast with Prophet Tool

This article is by Timothy Lam and originally appeared on the Alteryx Data Science Blog here: https://community.alteryx.com/t5/Data-Science-Blog/Expand-Your-Predictive-Palette-III-I-Sales-Forecast-with-Prophet/ba-p/504651

 

At a high-level, forecasting techniques can be broken down into three main categories:

Historical Average with Sliding Windows

Examples: Seasonal Decomposition, Exponential Smoothing (ETS)
Pros: Simplicity: any tool can integrate like Excel & Tableau;
Cons: Laggardly reaction to changes & overly responsive with outliers, only works with simple structure

Linear Models

Examples: ARIMA, VAR(Good for multiple time series)
Pros: Works with consistent variation, such as established seasonality trends
Cons: Proper assumption on stationarity and homoscedasticity (statistics term for consistent variance/error). Careful predictor selection to prevent multicollinearity (statistics term when predictors are linearly correlated).

Non-Linear Models

Examples: Prophet, GARCH (generalized autoregressive conditional heteroskedasticity), Deep Learning
Pros: Discovering non-linear relationship & data drift in your data.