# Tag: Forecasting (38)

**Forecasting Stories 5: The story of the launch**- Feb 18, 2021.

New products forecasting can be very difficult - there is no history to start with, and hence no base line. The number of assumptions can be huge. The best way to forecast then, is to try parallel approaches, build different views and triangulate on a common range.**Backcasting: Building an Accurate Forecasting Model for Your Business**- Feb 5, 2021.

This article will shed some light on processes happening under the roof of ML-based solutions on the example of the business case where the future success directly depends on the ability to predict unknown values from the past.**Metis Webinar: Deep Learning Approaches to Forecasting**- Jun 4, 2020.

Metis Corporate Training is offering Deep Learning Approaches to Forecasting and Planning, a free webinar focusing on the intuition behind various deep learning approaches, and exploring how business leaders, data science managers, and decision makers can tackle highly complex models by asking the right questions, and evaluating the models with familiar tools.**Forecasting Stories 4: Time-series too, Causal too**- Jun 1, 2020.

This article is about the story of taking effective business decisions basis a combined model. Let us together study how these components work hand in hand.**Forecasting Stories 3: Each Time-series Component Sings a Different Song**- May 8, 2020.

With time-series decomposition, we were able to infer that the consumers were waiting for the highest sale of the year rather than buying up-front.**Outbreak Analytics: Data Science Strategies for a Novel Problem**- Apr 30, 2020.

You walk down one aisle of the grocery store to get your favorite cereal. On the dairy aisle, someone sick from COVID-19 coughs. Did your decision to grab your cereal before your milk possibly keep you healthy? How can these unpredictable, near-random choices be included in complex models?**LSTM for time series prediction**- Apr 27, 2020.

Learn how to develop a LSTM neural network with PyTorch on trading data to predict future prices by mimicking actual values of the time series data.**Forecasting Stories 2: The Power of a Seasonality Index**- Apr 14, 2020.

Read this second entry in a series on time series analysis and seasonality, and see how, through 2 simple use cases, the power of a seasonality index is uncovered.**How (not) to use Machine Learning for time series forecasting: The sequel**- Mar 30, 2020.

Developing machine learning predictive models from time series data is an important skill in Data Science. While the time element in the data provides valuable information for your model, it can also lead you down a path that could fool you into something that isn't real. Follow this example to learn how to spot trouble in time series data before it's too late.**Predicting the President: Two Ways Election Forecasts Are Misunderstood**- Mar 27, 2020.

With election cycles always seeming to be in season, predictions on outcomes remain intriguing content for the voting citizens. Misinterpretation of election forecasts also runs rampant, and can impact perceptions of candidates and those who post these predictions. A better fundamental understanding of probability can help improve our collective notion of futurism, and how we monitor elections.**Forecasting Stories: Is it seasonality or not?**- Mar 17, 2020.

Kicking off with a series of forecasting stories, starting with seasonality and its business applications. This first article speaks of course corrections that were based on weather and calendar driven seasonality.**Stock Market Forecasting Using Time Series Analysis**- Jan 9, 2020.

Time series analysis will be the best tool for forecasting the trend or even future. The trend chart will provide adequate guidance for the investor. So let us understand this concept in great detail and use a machine learning technique to forecast stocks.**Webinar: Data-Driven Approaches to Forecasting**- Sep 19, 2019.

Whether it’s demand forecasting, supply chain management, or any other application, getting it right requires balancing the need for performance with the constraints of implementation and complexity. Learn more in this free webinar, Data-Driven Approaches to Forecasting, Sep 26.**Detecting stationarity in time series data**- Aug 20, 2019.

Explore how to determine if your time series data is generated by a stationary process and how to handle the necessary assumptions and potential interpretations of your result.**A Data Scientist’s Path to Understanding Market Simulation**- Jul 1, 2019.

Made possible by recent advances in computing power and machine learning, market simulation employs agent-based modeling, behavioral science and network science to recreate the complex dynamics and rules of how a population of people in a given market behave, influence each other and make decisions.**6 Industries Warming up to Predictive Analytics and Forecasting**- May 22, 2019.

Here are six sectors that are realizing how beneficial predictive analytics could be, embracing the possibilities of valuable insights extracted from such technology.**How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls**- May 10, 2019.

We outline some of the common pitfalls of machine learning for time series forecasting, with a look at time delayed predictions, autocorrelations, stationarity, accuracy metrics, and more.**How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy**- Jan 23, 2019.

We explain how to retrieve estimates of a model's performance using scoring metrics, before taking a look at finding and diagnosing the potential problems of a machine learning algorithm.**An End-to-End Project on Time Series Analysis and Forecasting with Python**- Sep 3, 2018.

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.**Data Science Predicting The Future**- Jun 19, 2018.

In this article we will expand on the knowledge learnt from the last article - The What, Where and How of Data for Data Science - and consider how data science is applied to predict the future.**Sales forecasting using Machine Learning**- May 8, 2017.

SpringML inviting business and sales leaders to its Man vs Machine Forecasting Duel - give them a day with your data and they will provide an algorithm based, unbiased forecast.**Introduction to Forecasting with ARIMA in R**- Jan 16, 2017.

ARIMA models are a popular and flexible class of forecasting model that utilize historical information to make predictions. In this tutorial, we walk through an example of examining time series for demand at a bike-sharing service, fitting an ARIMA model, and creating a basic forecast.**Data Science + Criminal Justice**- Oct 17, 2016.

The nation needs brilliant, creative minds to lead the next generation of crime forecasting. Enter the competition sponsored by National Institute of Justice to help improve policing and public safety with data science. $1.2 Million will be awarded.**Neural Designer: Predictive Analytics Software**- Sep 26, 2016.

Neural Designer advanced neural network algorithms, combined with a simple user interface and fast performance, make it a great tool for data scientists. Download free 15-day trial version.**NIJ Crime Forecasting Challenge – help improve policing and public safety with data science!**- Sep 22, 2016.

The nation needs brilliant, creative minds to lead the next generation of crime forecasting. Enter the competition sponsored by National Institute of Justice to help improve policing and public safety with data science. $1.2 Million will be awarded.**Free MOOC: Business Analytics Using Forecasting – enroll now**- Aug 17, 2016.

A new iteration of a MOOC on business analytics using forecasting gets underway in October. Enroll today!**Boost your Business Analytics Skills**- Jul 26, 2016.

Learn the latest business practices, concepts, methodologies and techniques in advanced analytics, data mining, survival analysis, explaining analytics to decision makers, fraud detection, and more with the SAS Business Knowledge Series.**Data Science Data Logic**- Sep 17, 2015.

Even though participating in MOOCs and online competitions are good exercises to learn data science, but it is more than algorithms and accuracies. Understand how to formulate hypothesis, data creation, sampling, validation, etc. to become true data scientist.**Upcoming Webcasts on Analytics, Big Data, Data Science – Sep 8 and beyond**- Sep 7, 2015.

The Future of Data Science, Ensuring Business Value from Analytics, Apache Ignite, Text Analytics, Best Practices of Data Science, Forecasting With Predictive Analytics, and more.**Interview: Ali Vanderveld, Groupon on Vital Ingredients of Analytics-powered Sales Force**- Jul 16, 2015.

We discuss the role of Analytics at Groupon, deciding factors for merchant priority, limitations of historical data, optimizing the efforts of sales force, data characteristics and dealing with Data Sparsity.**Guiding Principles to Build a Demand Forecast**- May 4, 2015.

Demand forecasting is key for many industries, including finance, healthcare, and retails, and it is one of the most challenging tasks for predictive analytics. We review challenges and guiding principles of demand forecasting.**Amazon: Research Scientist, Forecasting**- Sep 29, 2014.

We develop sophisticated algorithms that learn from large amounts of past data, such as prices, promotions, similar products, to forecast the demand of over 10 million products.**Kaggle Epilepsy Seizure Prediction Challenge**- Aug 28, 2014.

Create a forecasting system for predicting epileptic seizures in this Kaggle challenge to help improve the lives of epilepsy patients and win prizes. Competition ends on November 17.**SAS Analytics, Data Mining, Statistics Training – Limited Time Offer**- Aug 12, 2014.

SAS is celebrating training a million users! Don’t miss your opportunity to experience the SAS Business Knowledge Series with this special offer. Register by Aug 31 with promo code BKS1M and save.**Join the brightest minds at Analytics 2014**- Jul 29, 2014.

The Analytics 2014 conference hosted by SAS will help you hone your skills, learn new techniques and widen your understanding of this complex and ever-changing field. Special KDnuggets discount.**XLMiner solves Big Data Problems in Excel**- Jun 26, 2014.

XLMiner, a part of Analytic Solver Platform integrated software for predictive and prescriptive analytics - forecasting, data mining, optimization and simulation, lets you solve small or Big Data problems in Excel.**ICON Challenge on Forecasting and Scheduling**- Jun 3, 2014.

ICON is a combined competition with both a machine learning component (predicting energy prices) and an scheduling component (using the predicted prices to schedule tasks on machines).**IARPA RFI: Emerging Events and Participating Entities**- Feb 25, 2014.

IARPA looks for creative ideas on the representation of emerging events and their participants, especially events that involve individual or small groups.