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.

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Seasonal and trend decomposition using loess

I have the following code for forecasting an intermittent seasonal time series with several zeroes in it. How do I assess the fit of the model on the training data? I just get the forecast with plot(...
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How to forecast intermittent demand when the future days with 0 demand are known in advance

I have an intermittent time series of the demand of some products. I have read some very useful answers (such as Forecasting Intermittent Demand with zeroes in times series ) as to how one would go ...
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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 ...
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Forecast the proportion of a product

my question is the following: I have to forecast the proportion of the sales of a product. For example: For product 17503 there are four types of categories A, B, C and D. The total demand of the ...
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multi-step forecast classification problem

There are multiple approaches for multi-step time series forecasting, mainly recursive strategy, independent and MIMO. If I want to predict whether an event is going to occur at some point in the ...
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How to measure/quantify a time series volatility?

So I have a discrete time series of, let's say, 90 days. A change in the time series equals to a change of ownership. So it can look like this: [A, A, A ..., B,C] ...
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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 ...
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Grouped Time Series Forecast when some of the nodes breakdown

I am attempting to do a grouped time series forecast in R using an ARIMA method at the base nodes. However at such a granular level, a few of nodes do not have enough data and so the auto.arima ...
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1answer
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Combining Intermittent Demand and ARIMA

I have a time series dataset, where a customer may purchase fuel one week and not purchase again for 2-3 weeks. I need to forecast when a customer is likely to purchase and how much they will spend. ...
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Combination of hierarchial time series forecasts with different methods - setting weights

I am trying to forecast the the number of orders for different products of a product group. I have the time series for each product. One of the problems is that some/most time series are intermittent ...
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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 ...
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what does the constant term in the Moving average model represents?

that equation is gotten from here. Is that mean term represents the best fit for the bias term for MA model gotten by minimizing the mean squared error equation?
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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 ...
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time series forecasting in R for a period less than 2 years(18 months) which is totally random

I'm working on a project of forecasting. I have the count of the purchase order for an 18 months period of time. I'm attempting to create a forecast from time series data that has observations only on ...
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2answers
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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 ...
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1answer
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Modelling rare outcome in treatment evaluation

This question is related to my previous question. I am conducting a treatment evaluation in a retrospective cohort study. My dataset has 2000+ cases, each with 48 monthly observations (24 pre- and 24 ...
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Best approach for count prediction in time-series?

I have a dataset, which contains DateTime, target, target_type. ...
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2answers
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Weather data in time series predictions

Disclaimer: I know this is a long-ish post but I don't need code solutions just high level general direction approaches that are usually used in situations like these. So let's say I want to predict ...
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1answer
154 views

Extreme peaks if forecasting slow movers

to avoid negative forecasts, in this post it was mentioned to increase each value with a small amount. With the following example I am running into trouble doing this. This is not a forecast running ...
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1answer
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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 ...
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1answer
601 views

How to check if the data is intermittent or too many zeros are due to seasonality?

I have a dataset for weekly number of calls to a call center for three years.The data is seasonal (I know this from practitioners knowledge) which means that calls normally come on summer and winter. ...
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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....
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1answer
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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 ...
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1answer
338 views

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 ...
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Is the Poisson distribution suitable for intermittent, clumpy events?

Can I apply the Poisson distribution on the following type of data set? I have two types of processes, each generating events. The actual data that I posses are sets of these timestamps. The ...
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Time series for Intermittent (1 month per year) data in R

I am going to analyze some data for an intermittent operation using R. Let's say I operate a Xmas tree stand from Black Friday to Christmas Eve every year. Let's say I operate 150 different Xmas tree ...
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1answer
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ets() in R returns a flat forecast for intermittent demand

In my attempt to forecast sales demand by month utilizing the last 3 years of history to predict balance of the year, ets() from forecast() package yields an answer ...
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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 ...
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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 ...