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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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How to deal with zeros when using NBEATS to forcast demand?

So, I'm doing forecasting demand for a hotel using NBEATS, which is hierarchically organized. However, I'm facing an issue with the time series data at the bottom (room numbers), as they're filled ...
Jakov Gl.'s user avatar
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Complex Time Series Problem

I have a difficult time series problem that is stumping me. I have registration data for a repeating annual event. The date of the event changes every year as does the open period for registration. ...
Windstorm1981's user avatar
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Having problems with forecasting Intermittent Demand at Regular Seasonal Intervals With Daily Data [duplicate]

Like the title suggests I'm having difficulty deciding the next steps I should take in terms of my current project. At the moment I have about 4 years worth of daily sales data dealing with "...
tmoriss's user avatar
1 vote
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46 views

Flat window removal from time series

I have a time series that I'm using for forecasting and I'm facing an issue with a flat period. In my time series, I have the following dynamic: In the past the quantity was stationary (red part), ...
Flavio's user avatar
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1 vote
2 answers
442 views

Time Series Forecasting With "Seasonal Gaps"?

Suppose I have the following problem - we are interested in modelling the number of animals that cross a certain point each year (e.g. swim across a river). Let's say that these animals typically ...
stats_noob's user avatar
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Fitting an AR1 process to a sparse time series

Say I have a sparse time series, i.e., containing many zeros. Furthermore, the values in the time series can only be positive or zero. I want to test for the autocorrelation, i.e., fit an AR1 process ...
TylerD's user avatar
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0 answers
66 views

Intermittent Demand Forecasting for highly seasonal items

I have a dataset with intermittent sales and very high seasonality and I want to forecast that but it is my understanding that Croston's method only works on non seasonal items. Is there any other ...
Asma Ben Nasr's user avatar
1 vote
1 answer
966 views

Shall I use daily or monthly data for demand forecasting?

Let's say we want to forecast sku-level demand one year and four months ahead, and we have daily demand data for the last 3 years. Taking into account that most daily time series at sku-level contain ...
johndhd's user avatar
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1 vote
0 answers
276 views

Adding external factors to a time series model

I've been working on time series for a while now and using Hierarchical time series forecasting, Croston TSB methods for demand forecasting. I want to add external factors which affect the forecasting ...
Aakash Parsi's user avatar
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48 views

Identify trends from thousands of time series plots

Not sure if this is the right place to post this. I have like 100s of thousands of time series data and their respective graphs. I want to be able to classify the general trend for all of these curves ...
Mohamad Sahil's user avatar
1 vote
0 answers
42 views

Intermittent Electricity Output - Causal Effects

I am working on modeling the electricity output of a single power plant. More specifically, I am trying to compute causal effects of a variable prop on output. My model would look something like this $...
Philip Schnaars's user avatar
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How to predict unknown time series in using Facebook Prophet?

The problem is predicting hits on different stories published on a website. I am aware that for this kind of time series forecasting, facebook prophet is a popular library. However, it seems in ...
Della's user avatar
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3 votes
0 answers
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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 ...
Jivan's user avatar
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1 vote
2 answers
5k views

Croston method with python, demand doesn't effect forecast properly?

I tried to use croston method for intermittent forecasting via croston package which is available in below link: https://pypi.org/project/croston/ below code creates a sample ts and creates forecast ...
Tyr's user avatar
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3 votes
1 answer
863 views

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 ...
Afiq Johari's user avatar
1 vote
1 answer
149 views

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(...
user2371765's user avatar
1 vote
1 answer
894 views

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 ...
User2321's user avatar
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5 votes
1 answer
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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 ...
2 votes
1 answer
703 views

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 ...
awakened_iota's user avatar
1 vote
1 answer
2k views

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. ...
Mo van Praag's user avatar
2 votes
1 answer
220 views

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 ...
Folanir's user avatar
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6 votes
2 answers
8k views

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 ...
Hopes_High's user avatar
1 vote
1 answer
321 views

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?
Chaymae Ahmed's user avatar
4 votes
1 answer
9k views

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 ...
John M's user avatar
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1 vote
1 answer
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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 ...
Nitish Sherje's user avatar
0 votes
1 answer
106 views

With non-contractual customers, what method should I apply to calculate their demand for a specific time period viz. month, quarter

I have customer data for 18 months in the format as follows: ...
Nitish Pareek's user avatar
4 votes
2 answers
830 views

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 ...
Skander H.'s user avatar
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1 vote
1 answer
73 views

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 ...
C_H's user avatar
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7 votes
1 answer
4k views

Best approach for count prediction in time-series?

I have a dataset, which contains DateTime, target, target_type. ...
ultron's user avatar
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0 votes
2 answers
2k views

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 ...
veich's user avatar
  • 103
2 votes
1 answer
209 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 ...
Sven's user avatar
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7 votes
1 answer
3k views

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's user avatar
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9 votes
1 answer
6k views

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 ...
R. White's user avatar
  • 123
1 vote
1 answer
807 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. ...
Fairy's user avatar
  • 141
10 votes
1 answer
19k views

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....
Arushi's user avatar
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14 votes
1 answer
2k views

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 ...
Fikri's user avatar
  • 141
3 votes
1 answer
457 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 ...
CatsLoveJazz's user avatar
1 vote
1 answer
1k views

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 ...
cdmihai's user avatar
  • 175
1 vote
0 answers
221 views

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 ...
Dennis Conklin's user avatar
2 votes
1 answer
2k views

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 ...
user13296's user avatar
3 votes
2 answers
4k views

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 ...
João Daniel's user avatar
21 votes
1 answer
12k views

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 ...
Ed Hyer's user avatar
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