Questions tagged [forecastability]
Forecastability refers to how well a time series can be forecasted. It is frequently expressed as a lower bound in the achievable forecast accuracy for some error measure.
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Check if my time series is forecastable using Shannon entropy
According to this answer: https://datascience.stackexchange.com/a/95232/141037, is possible to verify the forecastability of a time series using the Shannon entropy, the lower the Shannon entropy ...
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When do you know if you can discard data during the estimation of a model's order and its parameters?
I have been working with forecasting for a short while, and one thing has been clear so far: each problem is unique because data to each problem are unique. I find the variety of forecasting methods ...
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Definition of "unpredictable"
How do we rigorously define the term "unpredictable" in cases of point and density prediction?
The term "unpredictable" is employed in various contexts, e.g.
"the outcome of ...
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How do you use mathmatical way to know that your machine learning problem is hopeless?
My question come across from this post: How to know that your machine learning problem is hopeless?
Is there any mathmatical or statistical way to prove that my machine learning problem is hopeless?
I ...
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Is my time series predictable?
I am new to time series and would appreciate help in this matter.
I have a time series with the following graph as a result of applying the plot_acf in Python. ...
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Demand classification
Most of the industries use a following approach to classify the demand pattern.
Smooth demand (ADI < 1.32 and CV² < 0.49).
Intermittent demand (ADI >= 1.32 and CV² < 0.49).
Erratic demand (...
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How good are inflation expectations as a predictor on inflation?
I want to research inflation expectations and how well they predict inflation. I have found some past articles but none of them explain how to forecast inflation using inflation expectations. I have a ...
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Contradiction in ARIMA: How does ARIMA predict a stationary series?
I know that for ARIMA to run, the series first needs to be made stationary using differencing. But stationary series are not predictable. So, how is this happening actually?
Quote from Stationarity ...
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Statistical terminology for the "difficulty" of an estimation task
I am looking for the proper statistical terminology to express the fact that one estimation task maybe intrinsically harder to solve than another task. Intuitively, I would characterize this property ...
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How to practically apply Lewandowski algorithm?
I am studying forecasting based on Lewandowski algorithm. I have an article that introduces about Lewandowski algorithm, but I do not understand how to apply in practical, especially model it in Excel ...
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Ways to increase forecast accuracy [closed]
Situation
Our use case:
demand forecasting for sales and operations planning
monthly granularity, ~5 years worth of historical data available
goal is to forecast future time windows of 1, 3 and 12 ...
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Real world time series
I am not used to time series forecasting, so I feel sorry that my question might be stupid.
Now i'm dealing with real world time series data, which is very short.
I want to know what method I should ...
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Time series: how much past predicts future
In financial (time series) statistics and forecasting we usually assume that the past of a series can predict the future to some extent. Every financial ad will warn you that investors should not ...
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Is CPU utilization a predictable time series? [closed]
I've been wondering whether metrics about CPU and resource utilization is a time series which can be predicted or rather a random walk which I cannot learn from. Can recognizing a pattern in the data ...
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Time Series Forecast for a time series getting updated
I am working on a forecasting problem, where i am planning to forecast the value for the current time step (real value 43 in data below in a[4] column). The data is in the form of values at each ...
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Forecasting with Irregularly Missing Data
Suppose I am supplying $N = 1000$ vendors, and I am looking for a way to predict their demand for my product over $T = 90$ days. Concretely, I hope to take some features for each vendor, such as their ...
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High peaks at same fixed lag in both acf and pacf of residuals of model from auto.arima and tbats output. Really stuck with this one
I have data for every 15 mins for 4 years. ADF test shows that my data is stationary. I tried fitting model using auto.arima and seasonal=F,and I get the output as ARIMA(3,1,2) but the residual acf ...
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Forecasting time point not value
I have a simple question.
when we want to forecast a time series, we always focus on the value of series in future. But could we forecast time point of spesific value? For example I would like to ...
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I need help with choosing a mid-long term forecastic method for this demand
I am trying to forecast the demand of a product for the next 36 months, based on its sales history. The demand plot is shown below.
I honestly don't know what to do with it. I tried linear and non-...
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Relationship between forecast bias and accuracy for situations with constrained supply
Consider a forecast process which is designed to create unconstrained end-customer demand forecast. This means that the forecast generation process does not consider supply or distribution constraints....
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How to restrict forecasts from a VAR model to be nonnegative?
I have two variables, assets and operating expenses year-ends for roughly 20 years. I would like to fit a VAR model with these two in order to forecast a couple years ahead.
1) The trouble is that ...
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Forecasting with a simple OLS regression
Suppose, we wish to predict future observations, e.g., by estimating the following simple AR(1) model with OLS: $Y_t=\beta_0 + \beta_1 \cdot Y_{t-1} + u_t$. Further assume that $E[u_t|Y_{t-1}]\neq 0$. ...
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Seeking advice on using time series estimation, for a specific type of data
I am new to time series modeling and am trying to come up with a solution for a problem. My problem is about estimation of next value in a time series, which is made of four components. One is picked ...
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Is it possible to do an adequate prediction on tiny sample?
I have a financial time series, x, it's length is n=8 observations only. Each observation corresponds to the quarterly costs (...
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How to know that your machine learning problem is hopeless?
Imagine a standard machine-learning scenario:
You are confronted with a large multivariate dataset and you have a
pretty blurry understanding of it. What you need to do is to make
predictions ...
Tim♦
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How to determine Forecastability of time series?
One of the important issues being faced by forecasters is if the given series can be forecasted or not ?
I stumbled on an article entitled "Entropy as an A Priori Indicator of Forecastability" by ...
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Forecastability and Coefficient of Variation
I'm trying to get a sense check here. When determining "forecastability" for sales data, I tend to use the CV. However, this is highly susceptible to seasonality and outliers. As such, I was wondering:...
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Assessing forecastability of time series
Suppose i have a little over 20.000 monthly time series spanning from Jan'05 to Dec'11.
Each of these representing global sales data for a different product. What if, instead of computing forecasts ...
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