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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 ...
george1994's user avatar
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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 ...
Urwa Shanza99's user avatar
2 votes
1 answer
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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 ...
bodhi's user avatar
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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 ...
monkey's user avatar
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2 votes
1 answer
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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 ...
Guillaume's user avatar
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1 answer
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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 ...
Maxime Hautin's user avatar
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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) ...
Philipp's user avatar
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4 votes
2 answers
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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 "...
Justin Furlotte's user avatar
2 votes
1 answer
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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 ...
Sane's user avatar
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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 ...
Putra R's user avatar
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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 ...
Dũng Chế's user avatar
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1 answer
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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 ...
Álvaro Méndez Civieta's user avatar
2 votes
1 answer
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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-...
Richard Hardy's user avatar
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Adjusting a multivariate predictive model for drifting seasonalities

This question is a repost of a question originally asked in Quantitative Finance. I was alerted that this would be a more appropriate place for it. I have a time series of daily observations that get ...
Guillermo 's user avatar
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1 answer
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How to determine the optimal lag length in time series?

I am a beginner in time series analysis, and I am always having this problem of selecting the optimal lag length for my time series, especially when using machine learning algorithms for the ...
jairiidriss's user avatar
1 vote
1 answer
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Scaling Out-of-Sample Forecasts in a Model with Normalized Variables: Reverting to Original Scale

I'm working on making forecasts using a model where variables were scaled by $$ \tilde x_i = \frac{{x_i - \text{mean}(x_i)}}{{\text{sd}(x_i)}} , $$ and I've saved the mean and standard deviation. Now,...
george1994's user avatar
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Why Do AR-NN Models Have Tighter Confidence Intervals Compared to Linear AR Models?

I have conducted a forecast for the following data series using different autoregressive models: Intercept-only, AR1, AR2, ARIMA BIC, ARIMA AIC, and AR-NN. Using the point forecasts, the AR1 model is ...
george1994's user avatar
4 votes
1 answer
118 views

Choosing Between Intercept-Only and AR-NN Models: Justified to not use the model with the lowest RMSE/MAE?

I have created two autoregressive models for forecasting: a basic intercept-only model and an AR-NN (autoregressive neural network) model. Both models show similar performance based on recursive one-...
george1994's user avatar
2 votes
1 answer
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How to Predict a Growing Time Series with Changing Slopes in Python?

I have a univariate time series dataset that represents a continuously growing trend, but the slope changes at different intervals. I want to predict future values of this time series. Here are some ...
Amine Boutaleb's user avatar
1 vote
1 answer
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How do I calculate estimated variance for an ensemble forecast?

I have several (n) different forecasts of comparable quality for a variable, based on the same data but using wildly different statistical models. For each, I have generated an estimate for m periods ...
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Prediction Intervals for forecasts vs Probabilistic Forecasting

In the context of time series forecasting, there a relationship between these 2? Are prediction intervals are type of probabilistic forecasting?
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Converting quarterly growth rate forecasts to yearly growth rate forecasts

I'm generating forecasts on a quarterly basis, focusing on metrics like the GDP growth rate for Brazil. These forecasts are presented as growth rates. For example, the forecast for 2020Q1 represents ...
Afiq Johari's user avatar
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Using results of log-log regression for forecasting

If I have a regression in with logs taken of both sides to give the equation: $ln(y) = \beta_0 + \beta_1 ln(x)$ and need to calculate the expected change in $y$ for a given $x$. I can see from reading ...
Paranoid Android's user avatar
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Reversing a First Difference Error Correction Model: Converting the Forecasted FD results back

I'm doing a study on how market rates affect interest rates. The model I've chosen is an Error Correction Model. I address the unit root issue in my dataset by taking the first difference. I then ran ...
dsupin's user avatar
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1 vote
1 answer
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How to adjust hyper-parameter values of SARIMAX as we move month on month

I am trying to build a SARIMAX forecasting model to forecast availability of technicians across all 50 US states over 12 weeks horizon. I have a seasonal data hence going in with SARIMAX. Sample data ...
Karthik S's user avatar
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18 views

Interpretation of time series spectral entropy values wrt forecastability by a general neural network

I recently started using spectral entropy to analyze time series (already windowed). I'm having difficulty for interpreting the results, the entropy of the last 25% of a series is 0.19, and the ...
Marco's user avatar
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How to extrapolate/forcast data with a linear mixed model

I built a linear mixed model with data from a cohort of patients followed from 3 years before the start of the treatment to 24months after. The variable of interest in their blood pressure. I would ...
Jilano's user avatar
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Forecasting ARIMA(0,0,1) model by hand - trouble with MA elements

I am having issues forecasting a stationary ARIMA(0,0,1) time-series. I currently have 500 observations of the process, and want to predict the coming three periods. Using Stata software, I am able to ...
Ohoma's user avatar
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1 vote
1 answer
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ARIMAX-GARCH flattens when using daily return, but not level

Whenever I do my ARIMAX-GARCH model for forecasting n-ahead with sentiment from news as my exogenous variable, the predictions seems normal when forecasting using level price of the stock, but it ...
BarneGeniet's user avatar
1 vote
1 answer
41 views

Strange increasing in $R^2$ when MAE and RMSE worsened for OLS

I am currently working on my thesis, which involves using machine learning to predict non-stationary and seasonal time series. I am encountering some results that I cannot explain. While I cannot go ...
M. Hansen's user avatar
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21 views

Importance of stationarity for ARIMA/ARIMAX/SARIMAX for predictive purposes

I am doing a forecasting project right now and I could use some understanding of why stationarity is importance when forecasting in general. Especially for the SARIMAX model. I know the problem of ...
Mathias Nissen's user avatar
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ARIMA model underestimates

What steps should we take if my ARIMAX model consistently underestimates? Furthermore, should this underestimation be a significant concern in my analysis? Edit: I am doing an ARIMAX forecast where I ...
BarneGeniet's user avatar
7 votes
3 answers
171 views

Paradox of Brier skill score of perfectly calibrated output?

Given outcomes, $y \in \{0,1\}$ and outputs $o = f(x) \in \mathbb R, o \in [0,1]$, I'm interested in the case where the model $f$ perfectly models the variable $Y$. Since $Y$ is Bernoulli, this means $...
Firebug's user avatar
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Predict COVID spread using latitude and longitude and time

I have a data that has latitude and longitude of individuals and the timestamp of geographical locations. I want to predict the spread of COVID using R using latitude/longitude and the time as well. I ...
William's user avatar
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Why the differenced at lag 12 time series of a SARIMA(0,0,0)(0,1,1)_12 model follow the MA(1) pattern with step 12?

I am trying to understand why the ACF of the seasonally differenced series reveals the AR of MA structure of the original series. For example: The following lines creates a SARIMA(0,0,0)(0,1,1)_12 ...
Epameinondas's user avatar
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What is the ARMAX model specification of the following economic setting?

I am currently doing a project estimating electricity prices in France, however, my modelling skills are lacking. I have hourly data on spot prices, which are determined per separate hour, one day ...
Zillah's user avatar
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2 votes
1 answer
65 views

Contradictory Sources on Seasonality being a nonstationarity

I have been trying to figure out whether seasonality means nonstationarity, and the answers from many (often reliable) sources seem to be contradicting. (lets define stationarity as weakly ...
da7666's user avatar
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Modeling non-negative time series with square root decay?

Q: How should I model a non-negative time series $y_t$ which exhibits square-root decay? More specifically, a time series $y_t$ whose square-root differences $\sqrt{y_t}-\sqrt{y_{t-1}}$ are linear and ...
lowndrul's user avatar
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2 votes
1 answer
23 views

Rolling forecasts where horizon is larger than step-size

Is it bad practice to perform rolling time-series forecasting where the forecast horizon is greater than the step-size? For example, if I have a model which produces weekly forecasts on a rolling day-...
ron burgundy's user avatar
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30 views

Why do we use only event history for temporal point processes?

I'm trying to understand temporal points processes. In particular, neural TPPs. In all the works I've read, the only features fed into the model are a sequence of event timestamps and marks if they ...
JulioHC00's user avatar
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Can we use decomposed time series in a test set to obtain models accuracy metrics?

Currently I'm cross validating (rolling time window, different test lengths) several forecast models to obtain performance comparison on highly skewed time series (due to true ocassional outliers). My ...
Tom's user avatar
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How to determine statistical significance for a time series and forecasts?

With a simple example of mortality rates, and a basic three-year mean baseline: ...
electronix384128's user avatar
4 votes
2 answers
54 views

Forecasting a series that comes with uncertainty

I have a time series resulting from a spatiotemporal aggregation on the spatial domain. As a result, I have a central measurement (let's say mean average) and a dispersion (let's say standard ...
Ricardo Barros Lourenço's user avatar
6 votes
2 answers
440 views

Re-selecting best sales forecasting model each month. Is this overfitting?

One of our teams works on sales prediction, and they run ~10 models each month for each product (+100). Then they use the best fitting model for next period prediction, which may be a totally ...
Luis's user avatar
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0 answers
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Different results when fitting ARIMA model for levels vs ARMA model for first differences in R

In the following code I show that I get different forecasts when fitting an ARIMA(2,1,0) for cumulative sums of a generated AR(2) model vs. fitting an ARMA(2,0) for the AR(2) itself. Can anyone point ...
Mr Frog's user avatar
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2 votes
1 answer
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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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Forecasting RNN and LSTM without X_test

Dear StackExchange Community, My data is composed of only 1 time series variable (Stock prices of an asset) I have splitted it to train and test subsets. I have tarined an RNN and LSTM models with ...
Amirgiano's user avatar
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A good variable to make a regression model for gas usage over the years for a city

so I'm new to statistics, I'm trying to make a regression model in Excel, explaining why, or due to what variable, does the gas usage change over the years. I tried using a basic Y variable - Time - ...
Nero's user avatar
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