Questions tagged [forecasting]

Prediction of the future events. It is a special case of [prediction], in the context of [time-series].

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Incorporate partial information about Y into predictions

I have a linear regression model predicting exports of toys from the United States on an annual basis. This initial model is based on a few factors: toy companies' demand projections, toy production ...
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Who estimated war casualties from tightly-controlled government news sources?

I've read about this historical case before so I thought it would be very easy to Google, but after a few dozen queries that come up with nothing relevant I'm ready to punt. The story goes like this. ...
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Update to Box and Jenkins Air Passenger data? [closed]

A textbook example of a time series is the Box and Jenkins Air Passenger data. In R you can get it with the command data(AirPassengers). It has the number of ...
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How can I predict a 2D array over time?

I've got a rectangular region that's composed of 1024 grid cells (32x32 cells). Each of these cells has an associated value, which then builds me a 2D array. For that 2D array, I've got 8 samples (...
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Optimization of Loss Function of Multivariate Forecasts

I am trying to write an optimisation function in R for the following problem, which aims to estimate the weights for a combination forecast based on the loss for the in-sample period $t = 1,...,T$. $\...
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Negative data for log transformation [duplicate]

I am analysing data on hourly electricity prices to try to do some forecasting, in my dataset I have mostly positive values, but some negative, around 20 per region for the two year duration. I want ...
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Forecasting at very granular level (sparse data) - how to aggregate or disaggregate? [closed]

I am trying to forecast the number of orders of laptops, which can have different combinations of features, like with x Ram, y color, z CPU, 13inc, etc, at the week level. And for this, I have my ...
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Poor auto-ARIMA predictions

I am trying to fit and forecast water production in a well and this accounts for my end of training thesis. But I got poor prediction from ARIMA and sarima models. I tried with auto ARIMA but it didn'...
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hard negative mining equivalent for prediction/forecasting problems

Input: past history 3D velocity components of an object up to time t; history size is w (window or lag) seconds in the past. Output: predicted future possible velocities for times in the range t < ...
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Predict user's next login time

I am trying to implement a model to predict the next time a user will login to some system. The only data I have is the user ID and the login time. The distribution for the time between each users' ...
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Deal with quarterly or monthly seasonality in forecasting a year ahead

Assume a time series with a clear seasonality with observations every quarter. If you want to use that time series and make predictions four steps ahead, but you are only interested in what the ...
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Global forecasting with Random Forests

The global approach for forecasting a group of time series involves training a single univariate model across all series, see Montero-Manso and Hyndman. Regression trees, e.g., Random Forests, perform ...
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how to use GAMs for seasonal time series forecasting m

I have a daily time series with multiplicative seasonalities : yearly, monthly and weekly seasonalities. I want to try a GAM model to forecast the time series I don’t know how the trend should be ...
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Forecasting Methods for "In Progress" Systems?

I'm working on stats/analytics for a logistics system. Projects materialize, go through a complex multi-step sequential system (can take weeks/months), and are finally realized as revenue after ...
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Computing reconciled prediction intervals when forecasting logged outcome variable using fable

EDIT: Is seems like the dev version of fabletools (.3.2.9000) includes the capability to do what I want via the boostrap option. I'm leaving this question unanswered until things get formally released....
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Difference between seasonality-adjusted data and the trend component

After decomposing the time series, what is the intuitive difference between seasonality-adjusted data and the trend component (from decomposition)?
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AR And MA Order

I have a time series which has sequence as follows Upon eye balling this series , it sounds me a series which has 20 cycles where frequency/counts of event increase from 0 and then decrease after ...
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How to interpret ACF and PACF in time series?

In this following monthly sales time series, how can I interpret ACF and PACF? STL Decomposition ACF: PACF:
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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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Forecasting two dependent count variables using single independent variable (Time)

I'm stuck with a modeling problem without any results in my org. The model first should be developed based on time (in M or W) in x axis and booking counts across y axis. Next it has to be identified ...
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Generate prediction intervals for a simple moving average model

I'm using a simple moving average to generate a forecast. Say I have $t$ observations. Then the forecast for time $t+1$ is given by \begin{equation} \hat{Y}_{t+1}= \frac{Y_t+Y_{t-1}+\dots Y_{t-m+1}}{m}...
3 votes
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Interpreting the output of scoring rules

Assume we have a probabilistic forecast for a continuous variable. Now we want to validate how good our estimate was. For that, we can use various scoring rules (e.g. CRPS, logarithmic score) or if we ...
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RNN: How to feed multiple different time series to the network?

I'm new to RNN's and asking for advice on how to feed data to an RNN such as a LSTM. I'm trying to forecast wind speeds at one location. For this location i have historical values as a time series. ...
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Is time series regression closer to forecasting or classification?

I'm working on multivariate time series regression task. The literature for it seems quite light compared to the problems of Time series classification (using algorithm like rocket for example) and ...
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Forecasting model to categorize promotions - do I need one or two forecasting methods, one for prediction and one for inference?

I have a project where I'm trying to do two things: Create a forecast for sales Categorize the value for promotions The data has a weekly seasonality, but it's a totally irregular seasonality. I ...
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How can I condition an autoregressive vector model given past observations?

I'm trying to predict the scores of a soccer team in the next match using a VAR (Vector Autoregression Model). So my first attempt was to define the model as it follows: $$g_{1,t}=\alpha_{1}+\beta_{11}...
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Maximizing Number of Forecasts, but not too great a cost

I wanted to ask is there a name for the following problem: Suppose, I have a prediction model, which makes a prediction, when certain preconditions are met. Naturally, the goal is to maximize the ...
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Multivariate forecast vs. forecast with exogeneous factors

If I wanted to forecast sales in a grocery store and I knew factors that would influence grocery sales, would there be a reason why I should use a multivariate time series model vs. time series models ...
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How can I call a point in the future of a time series that I would like to forecast?

I'm a beginner in time series forecasting and I'm not sure if I understood correctly the meaning of forecast horizon. I was considering horizons as points in the future for which we would like to ...
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error metrics resulted from time series forecasting as feature

While reading a paper about Time Series Anomaly Detection (https://www.usenix.org/conference/atc19/presentation/zhang-xu) I came to this : "Forecasting error features: Following the prior work [...
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Simple mean model for time series prediction

I have some historical dataset where I have the measure of a concentrate at different times of the day and I just want to create a simple model to predict the measure in the future. So, the ...
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Methods for irregular time series data

I have a dataset that looks like this. ...
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Temporal cross-validation in forecasting: model selection, hyperparameter tuning and comparison to independent forecast

I'm mainly working with time-series models and want to make sure to build the correct model selection process. Let's consider a forecasting problem and I have two model candiates, Model A and Model B. ...
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time series forecasting: how to evaluate performance on test set

Performance during the training of a model for time series forecasting can be computed simply considering the difference at each time $t$ between the real value $y_t$ and the model predicted value $\...
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Forecast Assistance given Monthly Time Series Data

I'm looking for some help to determine what type of model I should use for the given data set: ...
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What does it mean forecast horizon in time series forecasting?

I'm a beginner in time series forecasting and I'm not sure if I understood correctly the meaning of forecast horizon. I was considering horizons as points in the future for which we would like to ...
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Scoring Probabilistic Forecasts - Can we infer a standard deviation from the 84.1 quantile prediction?

I am trying to compare forecasts of a series, and have several trained estimators which are deep neural networks with arbitrary architecture. I'd like to compare the accuracy of their probabilistic ...
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What does this statement about a forecasting lead time mean?

The ARPA-E PERFORM datasets produced by NREL contain both historical and forecast data on load, wind power, and solar power. NREL offers the following piece of information about the day-ahead forecast ...
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What exactly is a "persistent/persistence model"?

I'm new to time series forecasting and only got previous experience with image processing in therms of neural networks. My goal is to do create an ML forecasting model for time series data. Currently ...
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Learning a stochastic pattern in a count TS using DeepAR [duplicate]

I am trying to the learn the following pattern of count time series of vehicle demand every hour. The count time series is generated from a negative binomial distribution with parameters n = 9 and p =...
3 votes
2 answers
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R and Time Series Analysis; Suggestions for forecasting a series with a shock

I believe that the reprex below is self-explanatory. I would like to extend a monthly time series by forecasting the next 3 data points. The series is rather volatile and it spikes during the last ...
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Unexplainable cyclical patterns on prediction intervals for time-series forecasting using Extreme Gradient Boosting regressor

I am following the documentation of skforecast to make time-series forecasting using the Extreme Gradient Boosting regressor (i.e., ...
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For time series forecasting using machine learning, is feature engineering only needed for the y variable or is it needed for all x variables also?

Say I am trying to predict house prices (y variable) using population growth and GDP (x variables) using XGBoost or Neural Networks. All 3 are time series. I understand that I have to feature engineer ...
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For time series, is non stationary an issue for machine learning models like they are for traditional time series models like ARIMA?

Sorry if the question seems basic. I understand that non stationary data is a big issue for traditional time series forecasting methods like ARIMA and VAR but is it the same for machine learning ...
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How to determine the appropriate forecasting techniques for non-stationary univariate time series data?

I am new and I don't have background in time-series analysis or machine learning. Therefore, I am posting this question here: I have three time-series data. ...
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Having trouble finding the correct Auto ARIMA model to use

I have tried a lot of different models so far, but I'm having trouble finding the correct one. The problem is that RMSE is relatively high when I compare it to validation set. Here is the data: ...
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Forecasting retail product sales to inform how many of which product SKUs to manufacture

This is my first post here, and I'm coming in with a bit of a wicked 'problem'. Basically, I am writing a consultation-style report for a fashion brand based in London and I want to be able to ...
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multivariate vs multiple time series - term explanation and forecasting model

Here is the time series I have: (p1s is an abbreviation of product 1 sales in dollars) ...
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Learning stochastic pattern using RNN

I have a pattern of count time series of vehicle demand as shown below.The time series is generated as follows: Categorical Random Variable, x = {0,1,2} and p(x) = {0.6,0.3,0.1} low vehicles = 1 + x , ...
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Regressing Singular Spectral Analysis Eigenvectors on data

I'm trying to model some time series economic data using mostly regression with other independent variables following the Error Correction Model (or Engle Granger approach). The ultimate goal is to ...

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