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 do I write a mathematical equation for ARIMA (1,2,0) [closed]

Can Someone help me check this out do my equation model calculation right? PS: only one AR1, the coefficient for ϕ1=-0.3922
Quinn C's user avatar
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45 views

How much statistics knowledge is enough for studying time series?

I am interested in studying time series (both theoretical and applied), my background is in mathematics and my probability knowledge is well enough but I haven't studied stats deeply (only one course ...
Pat's user avatar
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ETS Confidence Intervals in R are several orders of magnitude larger than the time series itself?

I am forecasting a time series with confidence intervals using the ets model in R. Here is the time series: Running the following R code: ...
Justin Furlotte's user avatar
1 vote
1 answer
49 views

Violation of assumptions in Generalized additive models (GAMs)

I wanted to forecast Dengue count for the given weather data covariates: MeanT Mean temperature MeanRH Mean relative humidity MeanSM Mean soil moisture MeanVP Mean vapor pressure MeanWS Mean ...
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Which Forecast Evaluation Metric To Use?

It is a forecasting problem. I need an evaluation metric which penalizes under-predictions more than over-predictions. Also I want it's range in certain interval (say 0-100), so that it becomes easier ...
Shardul Pingale's user avatar
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Is there a correction for samples from a (linear) Prophet model when trained on an inhomogenous Poisson point process?

Facebook's Prophet is a popular modelling choice for time series forecasting in production due to many steps being automated (and thus convenient). This can sometimes lead to over-reliance on it when ...
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can I use causalimpact (BSTS) to forecast without an intervention?

As a data scientist without much formal training, I'm looking to get some professional feedback on the following question: Is it ever advisable to use causalimpact (or I guess BSTS generally) to ...
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Incorporating ARIMA errors in a linear regression model

I am working with economic data and trying to create a linear regression model for forecasting purposes. The dependent variable data is in terms of percentage change and I've differenced the ...
Amy K's user avatar
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Forecasting excess mortality with ARIMA model

I am using the forecast package by Prof Hyndman, and have had success fitting ARIMA models to excess mortality (from the COVID-19 pandemic) data. I am currently trying to produce plots for cumulative ...
Jina A.'s user avatar
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Multilayer Perceptron vs. Recurrent Neural Network for Time Series Forecasting: Utilizing Multiple Lagged Values

I am currently analyzing daily sales data for a product sold across multiple stores using a Multilayer Perceptron (MLP) model. For simplicity, let's assume it consists of a single layer, structured as ...
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Prediction Intervals and Alternatives to NHST for testing forecast accuracy between models

Here Rob Hyndman says In the predictive approach to statistics (McLean, 2000), problems of statistical analysis are viewed as prediction problems, and the central theme is a statistical (probability) ...
pandashelp's user avatar
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On unbiasedness of an optimal forecast

Diebold "Forecasting in Economics, Business, Finance and Beyond" (v. 1 August 2017) section 10.1 lists absolute standards for point forecasts, with the first one being unbiasedness: Optimal ...
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Extrapolating/forecasting treatment effects from difference-in-difference model

My goal is to extrapolate or forecast dynamic treatment effects into the future using a fitted model. My data consists of two groups (treated and control), seven time points, and a continuous outcome. ...
CaptainAardvark's user avatar
2 votes
1 answer
31 views

Diebold-Mariano test for one-step forecasts using Mean Absolute Scaled Error

This is my first time doing time series forecasting, so I am sorry for any inconsistencies in my question. But I have two different models that I want to compare. On Wikipedia, I read about Mean ...
Oskar Weber's user avatar
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weekly time series forecasting with exogenous variables retrain cadence

I have a weekly forecasting model, which has exogenous variables. The forecasting horizon is 8 weeks ahead - 8 data points. I do forecast (8 weeks) the exogenous variables to use them as input for ...
janicebaratheon's user avatar
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1 answer
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Approach to Handling Stationarity in Multi-dimensional Time Series Forecasting with AutoARIMA

I am working on a time series forecasting project for a meal delivery service that operates in multiple cities. The company has several fulfillment centers across these cities for dispatching meal ...
user172500's user avatar
2 votes
1 answer
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Can tbats forecast partial years [closed]

I have several years of daily data from August to December. I read that I can use tbats from the forecast package. Each year has approximately 153 days but ...
Salvador's user avatar
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What critical level to use in diagnostic tests for model selection in forecasting?

I have been reading Hyndman & Athanasopoulos "Forecasting: Principles and Practice" (newest edition here) recently, and I noticed something that I regard as a possible inconsistency. On ...
Richard Hardy's user avatar
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In prediction modelling, is it bad practice to combine differing sampling frequencies of covariates into the same model?

An example of this type of prediction task is modelling economic time-series data. Depending on the type of data being reported, the sampling frequency varies: GDP is reported quarterly, employment ...
ron burgundy's user avatar
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Approach for forecasting time-series with several time-series of different frequency

The time-series I'm looking to forecast consists of daily data. The other time-series are made up of each of daily, weekly and monthly levels. Top of mind approach is to use an MV regression, ...
Chris's user avatar
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Comparing forecasts under overlapping rolling evaluation windows

Two models with differing assumptions each purport to provide a forecast for a vector of values 1 year in the future. Each model has been run at the start of each month for three years: Run Date ...
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How to Evaluate a Single-Value Prediction for a 6-Month Period Against Historical Data?

I'm tackling a time-series forecasting issue with daily granularity, aiming to predict a single aggregate value that represents the total sum of incidents over a 6-month period. My approach involves ...
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Can SHAP values across different models in the same family of models trained on different datasets be aggregated?

Let's say I have time - series data for 100 products in a particular store. I fit 100 regression models to generate 1-step forecasts for these products. Let's say that all features are common across ...
Tamojit Maiti's user avatar
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29 views

Autoregession meets multiple regression? - Help with verbiage and approach

Needing some help with verbiage and opinions on how I am approaching this model. I have counts of people over the past 24 months. month count 1 100 2 105 ... ... 24 200 First, I reverse the ...
Tyler Brown's user avatar
8 votes
1 answer
284 views

Regression At Scale: Best Practices Around Ensuring Quality of a Large Numbers of Forecasts

Background Often I am forecasting possibly one up to a few dozen variables in a project, but I have an upcoming project that will involve forecasting thousands of variables. I have some ideas of my ...
Galen's user avatar
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2 votes
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What is the natural progression from discrete AR models into continuous time?

Lets say we want to predict a single target variable and we have 10 regressors/features. Assume we would like to predict 30 days ahead (daily predictions up to 30 days ahead) and our data is a daily ...
MilTom's user avatar
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1 answer
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Time series model selection based on demand

I am working on a time series forecasting project in my office. We have to forecast the demand for 10 products at each store level (no hierarchy). Just at store level is enough..We have 20 stores.So, ...
The Great's user avatar
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60 views

Inclusion of prior knowledge about forecast to a (Bayesian) time series model

I am trying to construct an inflation forecasting model with external regressors and seasonality. It came to my mind that a very useful information for prediction could be the consensus forecast of ...
PK1998's user avatar
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4 votes
1 answer
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Different result between ARIMA and auto.arima

I have conducted an analysis on a time series dataset, and the issue is that the 'manual' ARIMA (SARIMA) performed in Gretl gives me a model (3,0,1)(1,1,1) with all *** and AIC=1595.332. However, when ...
Leonard Banković's user avatar
3 votes
1 answer
69 views

Density Forecasts with GAMLSS

Does someone know the function to create density forecasts within the GAMLSS Package? The predict. Formula is not the right one. Predict do Point Forecasts
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Diebold-Mariano Revisited: what is a reasonable parameter count for information criteria when the model is complex?

Diebold (2015) wrote a follow-up paper/essay reflecting on how his work with Mariano to develop the Diebold-Mariano test has been abused over the years. One of the main points in the follow-up paper ...
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1 vote
1 answer
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Symmetric AND Weight MAPE Calculation

I'm responsible to forecast a portfolio of consumer products on a monthly basis, and in calculating forecast accuracy, I'm lead to the MAPE (Mean Average Percent Error), which is useful, but has, ...
Mark J's user avatar
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6 votes
2 answers
1k views

Low hanging fruits for a simple NN

I have only the basic understanding of Neural Networks (NN). Recently, I encountered a scenario at my company where a team was using linear regression (LR) to forecast an important continuous ...
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3 votes
1 answer
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OLS Forecasting Intervals

currently studying for my econometrics exam and struggling to understand the difference between these two forecasting intervals. xf are new observations added to the sample. Could someone please ...
Quack's user avatar
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How would you argue against the bold statement that stationary is needed for sensible predictions

We talked about a (time series) process generating observations. Somebody gave the bold statement: "Regression or predictions for general processes only make sense (generalize well) when the ...
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1 vote
1 answer
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Adjusted R2 for LSTM

Background: I am working on a problem, where I am making predictions for a time-series data. I am considering two approaches: Use LSTM, predict n samples using recursive strategy (suggested e.g. in ...
Michał Panek's user avatar
1 vote
2 answers
42 views

Gridsearch on ARIMA favours random walk

I am working on a time-series forecasting problem with ARIMA. Since long-term predictions were not good, I've started using a "rolling ARIMA" like explained here ...
nico_so's user avatar
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0 answers
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How to get an hourly forecast from mean, max forecast and historicals

I have hourly historical temperature curve for a month say January. I also have a monthly peak and a monthly mean forecast for March 2024 (two values). Using this - How can we get an hourly forecast ...
Vineet's user avatar
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1 vote
0 answers
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Decomposition time series

I am studying a daily dataset from a game which contains informations about the peak of players from 2013-2023. This is my first try applying decomposition to see how time series components behaves. ...
Racamposx's user avatar
3 votes
1 answer
470 views

How do quantile time series forecasts work?

My office leadership is interested adopting “quantile time series forecasting”, the idea is query the model to predict the 5th, 25th, 50th, 75th and 95th percentiles of an RV given features such as ...
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1 vote
1 answer
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ADI and CoV - Move thresholds based on dataset?

I am currently working on demand forecasting. During my research online I came to know about methods used to classify demand which helps us to focus on series which have better forecasting ability etc....
The Great's user avatar
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1 vote
1 answer
16 views

How does autoregressive training help limit compounding errors at inference?

I'm having a little trouble justifying something in my head and was hoping someone could provide some intuition? I understand for LSTM models or models that maintain some state about a sequence that ...
Kiernan McGuigan's user avatar
4 votes
1 answer
116 views

ARIMA model on yearly data using R

Im new to time series forecasting, and I was trying to model the folowing time series data using ARIMA model in R, so that I can predict for the future 10 time periods. data: cereal_dataset When I ...
Dominic Joseph's user avatar
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0 answers
7 views

How to Assess Predictive Potential in Time Series Analysis, Especially with Deep Learning? [duplicate]

In the realm of time series forecasting, how can one assess the predictive potential of a given time series? While traditional methods involve checking for stationarity and white noise characteristics,...
Gvj Raug's user avatar
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0 answers
25 views

Approaches for forecasting higher frequency data with mixed (high and low) freq exogenous variables?

I have y data at a daily frequency and a number of x variables at daily, weekly and monthly intervals. I'm looking to create a ...
Chris's user avatar
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70 views

The time series shows random walk behavior from PACF and ACF plot but adf test shows its stationary

I am new to time series and wanted to practice it by forecasting an hourly time series. The adf and kpss test results show that the series is stationary. ADF test results ...
Om Mali's user avatar
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Valid forms of exploratory data analysis for time series that don't assume stationarity?

Lets say we are given a time series sample and want to try to create a model to forecast future values of said time series When trying to build a model to forecast time series data, many statistics ...
QMath's user avatar
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0 answers
34 views

How to use LSTM, TFT, or other RNN with time series of different lengths

I have a dataset of financial transactions per day with the volume of the transaction and the price of the transaction. For each day I want to calculate the volume weighted average price at the end of ...
Mathematician....'s user avatar
1 vote
1 answer
53 views

Which machine learning methods that leverage historic and real-time data should be considered for timeseries short-term forecasting?

Some clarifications to my question: The data I have available for use is: (a) historic data of features and ground truth on 60-minute interval, (b) real-time data of features on 60-minute interval, (...
casaout's user avatar
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Avoiding negative values in Forecasting

I have questions about the Box-Cox transformation that can be used to maintain positive forecasts (Log-Transform) when $\lambda=0$ \begin{equation} w_t = \begin{cases} \log(y_t) & \...
Rashad Al-Harthy's user avatar

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