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Question 1

1vote

0answers

37views

Designing a demand forecasting model with a dynamic daily update and a final horizon prediction — best practices to avoid leakage?

I am working on a demand forecasting problem for ferry vehicle capacity.For each voyage, I have daily snapshots of the cumulative reservations from the opening date until departure day.So each ...

Analivia Valery

11

askedNov 7 at 15:53

Question 2

0votes

0answers

9views

Are there any other powerful optimization tools available besides the ABC and PSO algorithms? [duplicate]

What are other optimization tools that are powerful enough to improve the accuracy performance of the neural network model? Please give me recent tools that are powerful

bbadyalina

823

askedNov 5 at 1:10

Question 3

1vote

0answers

50views

What is the best statistical approach to forecast cash flow from run-off debt vintages with a growing balance?

community.I'm facing a modeling problem for cash flow forecasting and would like to know what the most robust mathematical/statistical approach is to solve it.The Problem: Debt Recovery Forecasting...

sn3fru

215

askedNov 4 at 21:54

Question 4

1vote

0answers

33views

Need advice on length of context for future prediction

I'm using a trained foundation model to forecast values on a time series. The model works by taking a window of recent data (context) to predict near-future outcomes (horizon).How can I know the ...

Michael

11

askedOct 30 at 20:52

Question 5

1vote

0answers

18views

How to use a hierarchical Bayesian model to combine regional and country-level data for TPES projections?

I’m trying to project TPES (Total Primary Energy Supply) by country in Africa up to the year 2100 under different SSP (Shared Socioeconomic Pathways) scenarios, the same framework used in the latest ...

grégoire david

11

askedOct 22 at 14:46

Question 6

1vote

1answer

46views

Forecasting with VAR models after differencing some time series

i have data from 85 participants who answered 6 items for 82 consecutive days. In other words: I have 6 time series per participant. I already imputed the missing data, so that all time series have ...

C K

61

askedOct 20 at 14:55

Question 7

4votes

1answer

147views

Is using contemporaneous components to forecast an aggregate a valid method or a form of data leakage?

I am in the middle of a deep methodological debate regarding a time series forecasting problem and would appreciate the community's expert opinion.The ContextI am trying to forecast an aggregate ...

PSE

298

askedSep 17 at 5:35

Question 8

0votes

0answers

48views

Sampling counterfactual posterior to mitigate error autocorrelation in event studies

I have question regarding event studies (pre-event data is observed, an event occurs at $t=e$, then following the treatment is assumed to be in-effect.)There are multiple approaches to event study ...

jbuddy_13

3,970

askedSep 12 at 23:24

Question 9

0votes

0answers

40views

Forecasting beta distributions

I hope you are doing well. I would appreciate your help with the following questions (listed at the end).ContextThere is a financial aid program that covers tuition fees for university students for ...

Mangostino

133

askedSep 11 at 21:49

Question 10

5votes

4answers

1kviews

What is difference between nowcasting and forecasting?

Recently I crossed to this Github Repo trying Benchmarking econometric using ML models in nowcasting GDP (see the paper).Q1: What is difference between nowcasting and forecasting over time data in ...

Mario

579

askedAug 27 at 15:22

Question 11

1vote

0answers

45views

Dynamic Linear Model: Classical vs Discount Approach

I'm working on a time series forecasting problem and trying to decide between the classical approach and discount approach for Dynamic Linear Models (DLMs).Anyone here has experience comparing these ...

Girigio

73

askedAug 21 at 21:41

Question 12

4votes

1answer

196views

Can I have some help choosing a very low-sample estimator?

I want to forecast what next semester's finances may look like, regarding my campus job. I get paid bi-weekly, and have eight past data points: ...

HydroPage

143

askedAug 15 at 14:06

Question 13

-1votes

2answers

191views

Quantile regression: But which quantiles (conditional on what)?

I have a task of making a quantile regression (5%, 50% and 95%) for tomorrow's power production. However, I am trying to grasp which quantiles we are talking about. Wikipedia (and similar sites) ...

pencil_sharpener

1

askedJul 31 at 8:21

Question 14

5votes

1answer

172views

Distribution based loss for regression with unbounded data

Currently I am dealing with time-series data conserning the power consumption of machines. Therefore, all target variables range from zero to infinity, technically ($y \in [0, \infty)$). The data ...

Blindschleiche

153

askedJul 30 at 8:41

Question 15

4votes

1answer

93views

Modelling cumulative numbers

Here is a dataset I have:...

user485246

askedJul 24 at 13:38

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Questions tagged [forecasting]

Designing a demand forecasting model with a dynamic daily update and a final horizon prediction — best practices to avoid leakage?

Are there any other powerful optimization tools available besides the ABC and PSO algorithms? [duplicate]

What is the best statistical approach to forecast cash flow from run-off debt vintages with a growing balance?

Need advice on length of context for future prediction

How to use a hierarchical Bayesian model to combine regional and country-level data for TPES projections?

Forecasting with VAR models after differencing some time series

Is using contemporaneous components to forecast an aggregate a valid method or a form of data leakage?

Sampling counterfactual posterior to mitigate error autocorrelation in event studies

Forecasting beta distributions

What is difference between nowcasting and forecasting?

Dynamic Linear Model: Classical vs Discount Approach

Can I have some help choosing a very low-sample estimator?

Quantile regression: But which quantiles (conditional on what)?

Distribution based loss for regression with unbounded data

Modelling cumulative numbers

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