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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How can you forecast transformed time series data?
I have time series data with both a trend and seasonal component. I removed this from the data using the following:
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One step ahead forecasts: Why is LSTM so much worse than XGBoost? [closed]
I am working on generating recursive one-step-ahead predictions for a time series y using a minimal set of regressors. I have found that linear models all perform similarly and fail to outperform ...
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How to Improve performance of deep learning timeseries forecasting model like LSTM? [duplicate]
I have historical data of 5 years (June 2019- June 2024). Data is in daily & csv file format. I have 4 features: Data, AQI, Raw Concentration, NowCast Concentration. I am trying to forecast only ...
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Steps for Forecasting with known copula's parameters
I want to calculate the Mean absolute percentage error (MAPE) for my copula model. I am stuck at the forecasting step. I am not specifying the copula here for different data pairs.
I have two time ...
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Combine back- and forecast errors for cross-validation
Suppose I have a procedure to predict the timeseries value $Y_{t+k}$, where $t$ is the current period and $k \geq 1, 2, \dots$. Now, I want to estimate the procedure's out-of-sample performance. The ...
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Very Specific Plateaus in Time Series Data
I am looking at time series data of the depth of water in different pipes. There is a rare occurrence where extreme amounts of water are trying to get into the pipe, but since it is full the water ...
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Forecasting Survival Analysis
I use the Kaplan-Meier estimator to represent survival functions between two groups. Suppose I have X events at a given time t. How can I predict time t+k to obtain X+i events?
As with time series, is ...
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Could we impose to a SARIMA model that the sum of predict values equals a given value (in R)?
I work on forecasting in R using a SARIMA model with monthly time series data. However, for the last year, I don't have the details by months but I have the annual value.
I want to predict 2023 data ...
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Can one determine the number of forecast/prediction steps in a VAR on a priori grounds?
Context of my question: I am running a vector autoregression (VAR) model using two time-series of equal length (n ~ 750 data points). The lag was chosen based on the Bayes information criterion (BIC) ...
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Using Bootstrapped Residuals to Estimate Time Series Prediction Intervals
I am working with a very simple forecasting "model" which is not a standard statistical model. I am trying to use the methodology described in Hyndman's textbook under the section "...
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Forecasting time series using simulations
Suppose we have a stationary time series $x_{1}, x_{2}, ..., x_{T}$. Goal is to forecast up to $T+h$, i.e., forecast $x_{T+1}, x_{T+2}, ..., x_{T+h}$.
Forecasting methodology:
Using econometric ...
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Time Series Forecasting Feature Engineering
I've performed feature engineering and modeling my data about daily retail store sales to do time series forecasting. I tried some scenarios of features that I use for modeling. One scenario that gave ...
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ARIMA Models Modifications [duplicate]
I'm current working on a project. This project specify in using ARIMA Models to predict the future value of variable 'cases'. After differencing the time-series to make it stationary, here is the ...
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Problems with prophet library for time series forecasting
I have a very small dataframe with two columns: time_key, which is a date and value which is a numerical variable meassuring the ...
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Why do top-down approaches produce biased coherent forecasts?
The context is forecasting hierarchical time series. Section 10.4 of "Forecasting: Principles and Practice" (2nd edition) by Hyndman & Atahnasopoulos states:
One disadvantage of all top-...
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