# 2437 search results for "time series"

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

January 26, 2017
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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...

## New Course: Introduction to Time Series Analysis

January 19, 2017
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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...

## 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...

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

December 6, 2016
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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...

## Cross-validation for time series

December 5, 2016
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I’ve added a couple of new functions to the forecast package for R which implement two types of cross-validation for time series. K-fold cross-validation for autoregression The first is regular k-fold cross-validation for autoregressive models. Although cross-validation is sometimes not valid for time series models, it does work for autoregressions, which includes many machine learning

## Forecast double seasonal time series with multiple linear regression in R

December 2, 2016
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I will continue in describing forecast methods, which are suitable to seasonal (or multi-seasonal) time series. In the previous post smart meter data of electricity consumption were introduced and a forecast method using similar day approach was proposed. ARIMA and exponential smoothing (common methods of time series analysis) were used as forecast methods. The biggest disadvantage of this...

## Global sea ice time series visualization sandbox with R, ggplot, and plotly

November 22, 2016
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In the past couple of weeks I’ve noticed a flurry of visualizations of global sea ice extent on social media. If you’re like me, and curious to see what the data look like yourself, here’s a bit of R code to fetch and visualize the most recent da...

## Endogenously Detecting Structural Breaks in a Time Series: Implementation in R

November 8, 2016
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The most conventional approach to determine structural breaks in longitudinal data seems to be the Chow Test. From Wikipedia, The Chow test, proposed by econometrician Gregory Chow in 1960, is a test of whether the coefficients in two linear regressions on different data sets are equal. In econometrics, it is most commonly used in time … Continue...

## Dual axes time series plots with various more awkward data

August 27, 2016
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In my most recent blog post I introduced the dualplot() R function, which allows you to create time series plots with two different scales on the vertical axes in a way that minimises the potential problems of misinterpretation. See that earlier post ...

## Five problems (and one solution) with dual-axis time series plots

August 19, 2016
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If you need to present two time series spanning the same period, but in wildly different scales, it's tempting to use a time series chart with two separate vertical axes, one for each series, like this one from the Reserve Bank of New Zealand: Charts like this typically have one or more crossover points, and that crossing imparts meaning...

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