2579 search results for "time series"

Forecasting: Time Series Exploration Exercises (Part-1)

April 10, 2017
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Forecasting: Time Series Exploration Exercises (Part-1)

R provides powerful tools for forecasting time series data such as sales volumes, population sizes, and earthquake frequencies. A number of those tools are also simple enough to be used without mastering sophisticated underlying theories. This set of exercises is the first in a series offering a possibility to practice in the use of such Related exercise sets:

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Financial time series forecasting – an easy approach

March 21, 2017
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Financial time series forecasting – an easy approach

Financial time series analysis and their forecasting have an history of remarkable contributions. It is then quite hard for the beginner to get oriented and capitalize from reading such scientific literature as it requires a solid understanding of basic statistics, a detailed study of the ground basis of time series analysis tools and the knowledge Related Post

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camsRad, satellite-based time series of solar irradiation

March 21, 2017
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camsRad, satellite-based time series of solar irradiation

camsRad is a lightweight R client for the CAMS Radiation Service, that provides satellite-based time series of solar irradiation for the actual weather conditions as well as for clear-sky conditions. Satellite-based solar irradiation data have been around roughly as long our modern era satellites. But the price tag has been very high, in the range of several...

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Simulations to explore excessive lagged X variables in time series modelling

March 11, 2017
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Simulations to explore excessive lagged X variables in time series modelling

I was once in a meeting discussing a time series modelling and forecasting challenge where it was suggested that “the beauty of regression is you just add in more variables and more lags of variables and try the combinations until you get something that fits really well”. Well, no, it doesn’t work like that; at least not in...

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Prophet: How Facebook operationalizes time series forecasting at scale

February 24, 2017
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Prophet: How Facebook operationalizes time series forecasting at scale

Facebook is a famously data-driven organization, and an important goal in any data science activity is forecasting. Now, Facebook has released Prophet, an open-source package for R and Python that implements the time-series methodology that Facebook uses in production for forecasting at scale. Prophet has a very simple interface: you pass it a column of dates and a column...

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Is my time series additive or multiplicative?

February 20, 2017
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Is my time series additive or multiplicative?

Time series data is an important area of analysis, especially if you do a lot of web analytics. To be able to analyse time series effectively, it helps to understand the interaction between general seasonality in activity and the underlying The post Is my time series additive or multiplicative? appeared first on Locke Data. Locke Data...

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Doing magic and analyzing seasonal time series with GAM (Generalized Additive Model) in R

January 26, 2017
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Doing magic and analyzing seasonal time series with GAM (Generalized Additive Model) in R

As I wrote in the previous post, I will continue in describing regression methods, which are suitable for double seasonal (or multi-seasonal) time series. In the previous post about Multiple Linear Regression, I showed how to use “simple” OLS regression method to model double seasonal time series of electricity consumption and use it for accurate forecasting. Interactions...

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New Course: Introduction to Time Series Analysis

January 19, 2017
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New Course: Introduction to Time Series Analysis

We just launched our newest time series course - Introduction to Time Series Analysis by David S. Matteson. Many phenomena in our day-to-day lives, such as the movement of stock prices, are measured in intervals over a period of time. Time series an...

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Two new online R courses on time series (via DataCamp)

January 18, 2017
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DataCamp recently launched two new online R courses on time series analysis. Introduction to Time Series Analysis What You’ll Learn: Chapter One: Exploratory Time Series Data Analysis (FREE) Learn how to organize and visualize time series data in R. Chapter Two: Predicting the Future Conduct trend spotting, learn the white noise model, the random walk model, and the definition of...

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Why time series forecasts prediction intervals aren’t as good as we’d hope

December 6, 2016
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Why time series forecasts prediction intervals aren’t as good as we’d hope

Five different sources of error When it comes to time series forecasts from a statistical model we have five sources of error: Random individual errors Random estimates of parameters (eg the coefficients for each autoregressive term) Uncertain...

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