Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [intermittent-time-series]

Intermittent time series are characterized by "many" zeros and "few" non-zero values. If they describe intermittent demand, they are typically integer-valued.

The most common method to forecast intermittent demands is , but methods are also used. In contrast, and work less well. A textbook on intermittent demand forecasting, especially in the context of inventory control, is Boylan & Syntetos (2021), Intermittent Demand Forecasting: Context, Methods and Applications.

Note that intermittent time series typically follow asymmetric noise distributions. Therefore, the Mean Absolute Error or Deviation may be optimized by a biased forecast. If you are aiming for an unbiased mean point forecast, it's probably better to use the Mean Squared Error.

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Analysis of time series with many zero values

This problem is actually about fire detection, but it is strongly analogous to some radioactive decay detection problems. The phenomena being observed is both sporadic and highly variable; thus, a time series will consist of long strings of zeroes…
Ed Hyer
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How to compare forecasting methods?

I have several intermittent data. Based on those data, I would like to compare several forecasting methods (Exponential Smoothing, Moving Average, Croston, and Syntetos-Boylan), and decide whether Croston or Syntetos Boylan is better than SES or MA…
Fikri
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Explain the croston method of R

I am using crost() function of R for analyzing and forecasting intermittent demand/slow moving items time series. I am having difficulty in understanding the output. Could anyone help in understanding the model in layman's terms. Below is the code…
Arushi
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Forecasting Poisson, accuracy and prediction intervals

I'm trying to forecast Poisson data, divided in groups, of 1-26 months of data, depending on the group. Of the pooled data 65% has a value of 0 and 25% a value of 1. I couldn't find any trends or seasonality, so I started to test a couple of…
R. White
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Best approach for count prediction in time-series?

I have a dataset, which contains DateTime, target, target_type. target is basically the count of a process. target_type is binary and it says if the count was of the type, say "outflow" or of type, say "inflow". this has been recorded at periodic…
ultron
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Forecasting models for time series with lots of zero values

The title is self explanatory: I am interested in which models are suitable for forecasting time series with a lot of zero values in it. Which forecasting models are recommended?
Ferit
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Books or articles to study different forecasting techniques for lumpy and intermittent demand

I am doing a project to forecast demand for an automotive firm making spare parts. Using average demand interval (ADI) and square of the Coefficient of Variation (CV2), I have categorized product SKUs into smooth, erratic, lumpy and intermittent.…
Sanket Verma
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Forecasting Intermittent Demand with zeroes in times series

I am trying to forecast intermittent demand (slow movers and extreme slow movers). Here's the type of data I am working with weekly data so I cannot really group it has zeroes in time series not sure if seasonal (at least not visibly) Based on…
Hopes_High
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Decomposing a time series with some zero values

There are many techniques to decompose a time series into trend, seasonal, and remainder components. I was wondering if these techniques can be applied without worry to time series which have some zero values. I think that the answer may be…
John M
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Survival analysis for an event with a possibly infinite lifetime?

I'm trying to see if it is possible to use some sort of survival analysis in the context of analyzing daily demand for very slow moving items (i.e. items where one or two units are sold every few weeks). In this scenario, it seems more reasonable…
Skander H.
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Measure of intermittency/continuousness of a signal

I have three signals (below) each having the same standard deviation, however, are clearly very different temporally. Is there some such metric that could be calculated for each of these signals to give an indication of how they vary over time,…
CatsLoveJazz
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How to detect intermittent time series?

I need to automatically identify if a time series is intermittent or not. Depending on the result I'll use one or another method for forecasting it. Is there any test to detect intermittent time series?
João Daniel
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Spot unusual patterns in a discrete intermittent time series

I have a multitude of daily time series representing the volume of a certain product arriving per day at a station. There are as many time series as their are stations, and they each look like the following: date volume 2020-01-01 …
Jivan
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Forecasting Sparse Demand Data: Cumulative sum transformation

I have many SKUs/products that have missing historical values. By missing, it means it has no data or order at all. I'm tempted to say intermittent but there are not regularly intermittent to make use of method like Croston model. Also it's not…
Afiq Johari
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Is it necessary to remove Seasonality while time series forecasting using ML methods ? Can't model learn it on itself?

I think ML model can learn from seasonal variations also. But if we remove seasonal variations, model & add it back, then essentially, we will end up dividing learning into : 'seasonal variations learning subtask' ' learning relations of…
awakened_iota
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