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...

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

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...

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...

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...

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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