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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Structuring of a large, multivariate, multi-site timeseries weather dataset for a machine learning project
I have access to a large timeseries weather dataset which has values for historical weather (temperature, pressure, humidity, wind, rainfall etc.) across the UK over a large timeframe (approx. 1853-...
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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 "...
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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), ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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
$...
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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 ...
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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 ...
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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 ...
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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 ...
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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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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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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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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:
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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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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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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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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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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?
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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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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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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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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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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 ...
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