A basic forecasting technique for time series data, optionally including trend and/or seasonality, but (usually) excluding causal influences.
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Multivariate exponential smoothing and Kalman filter equivalence
Suppose the time-series $X$ is hidden state Gaussian random walk and we observe $Y = X + e$, where $e$ is gaussian white noise independent of $X$.
The Kalman estimator of $X$ in this case has a ...
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Best practices for dealing with shifting, inconsistent seasonality
This question is related to a previous post I've looked at (Calculation of seasonality indexes for complex seasonality), but deals with more granular data (daily instead of weekly), and transforming ...
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127 views
Exponential moving average with sub-interval relevance / varying timeframe
I need to calculate an exponential moving average for a series of data. The intended sampling interval is fixed (say 1s) but the data stream has varying intervals (data intervals vary from 0.01s to ...
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51 views
Bootstrap Prediction Intervals
My question concerns the construction of forecast prediction intervals using bootstrapping.
I have a 36 month time series, which I am using to perform point forecasts for the next 12 months using ...
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39 views
Calculating price elasticity from triple exponentially smoothed values
I have a time series which exhibits a linear trend and multiplicative seasonality, so I've smoothed it using Holt-Winters exponential smoothing. Now, I suspect some of the deviation between smoothed ...
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201 views
Help choosing the optimal time series analysis package
I am developing an app for time series analysis that should support the following:
Exponential Smoothing (Holt-Winters)
Box-Jenkins
curve fitting (straight line, quadratic, exponential, growth)
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