2022 Developer Survey is open! Take survey.

Questions tagged [forecasting]

For questions related to forecasting of any type beyond basic forms of extrapolation, as applicable in fields such as financial planning, portfolio management tooling, automated driving or piloting collision avoidance (when the trajectories are not constant speeds along straight lines), weather science, climate projection as a function of carbon emissions, and such.

Filter by
Sorted by
Tagged with
1 vote
1 answer
54 views

ML model to predict timeouts

I am new to ML and am trying to build a model to predict timeouts for a website. The website is being monitored once a minute and the data consists of a timestamp and the response time in seconds. E.g....
user avatar
  • 11
0 votes
0 answers
11 views

How to approach in panel data using machine learning?

I have monthly electricity consumption data for the last year of 100k households. So there is a total (100k*12)= 1.2 million data points. I am willing to use this dataset to predict the individual's ...
user avatar
0 votes
0 answers
28 views

Time series forecasting for multiple objects with common features

I know the title of this question may raise an eyebrow, but I can't find the technical terms to define or investigate the actual problem. To demonstrate my problem with a simple hypothetical scenario: ...
user avatar
0 votes
1 answer
27 views

How can I use a prediction model (e.g., ARMA model or LSTM) for multi-variate data?

I have a question I have had a dataset below ...
user avatar
1 vote
1 answer
61 views

Why doesn't the LSTM model improve the time-series forecasting significantly with respect to the MLP model?

I have recently started learning time series forecasting. I have a dataset of the weekly payment history of 10k clients over 1 year, and I want to predict the future 5 payments for a test set of 1k ...
user avatar
1 vote
1 answer
42 views

What is a better approach to perform predictions of time-series several values ahead?

Suppose one has a time series (univariate or multivariate) and the goal is to predict values of these series several steps ahead. I see two possible strategies: Create a model (recurrent, ...
user avatar
0 votes
0 answers
20 views

What is the significance of the RegLoss colum in Neuralprophet

I recently made a forecast with neuralprophet and after training, I got a table with three columns; "SmoothL1Loss", "MAE" and "RegLoss". Please, I need to know the ...
user avatar
  • 1
0 votes
0 answers
11 views

What are the typical things in data that I would have to look for, when implementing survival models using machine learning?

Problem Scenario I am working on an industry-specific problem focussed on predicting the failure of a seal/gasket in the given time interval(T) in a high-pressure-compression environment. Whenever ...
user avatar
  • 111
1 vote
0 answers
39 views

LSTM Recursive Forecast

I am confused about the way the LSTM networks work when forecasting with a horizon that is not finite, but I'm rather searching for a prediction in whatever time in future. In physical terms, I would ...
user avatar
1 vote
0 answers
10 views

LSTM Forecast Evolution

I have a confusion about the way the LSTM networks work when forecasting with an horizon that is not finite but I'm rather searching for a prediction in whatever time in future. In physical terms I ...
user avatar
  • 11
1 vote
1 answer
61 views

Can an existing transformer model be modified to estimate the next most probable number in a sequence of numbers?

Models based on the transformer architectures (GPT, BERT, etc.) work awesome for NLP tasks including taking an input generated from words and producing probability estimates of the next word as the ...
user avatar
  • 119
0 votes
0 answers
49 views

Which is the best algorithm to predict the trajectory of a vehicle using lat/lon data?

I'm using Kalman Filter approaches and I've just implemented the extended Kalman filter (EKF) with my object 2D trajectory. However, I have a mess of alternative approaches that may fit better like ...
user avatar
  • 1
2 votes
1 answer
71 views

Should forecasting with neural networks only be treated as a supervised learning (regression) problem?

I have recently made a work about the application of neural networks to time series forecasting, and I treated this as a supervised learning (regression) problem. I have come across the suggestion of ...
user avatar
1 vote
0 answers
25 views

How to make a multivariate forecasting if one of features becomes known for the future with some confidence level, e.g. weather forecast data

Let's assume that we make forecasting of another metric partially based on forecasts of the weather forecast, e.g. of temperature, pressure, then we can potentially obtain those forecasts from one of ...
user avatar
  • 111
1 vote
0 answers
38 views

Is there a difference between using 1d conv layers and 2d conv layers with kernel with size of 1 along other than time dimension?

Let's assume I use convolutional networks for time-series prediction. Data I feed to the network have 1 channel depth, height of number of periods and number of features is the width, so the frame ...
user avatar
  • 1,027
2 votes
3 answers
201 views

Using sigmoid in LSTM network for multi-step forecasting

I'm trying to develop a multistep forecasting model using LSTM Network. The model takes three times steps as input and predicting two time_steps. both input and output columns are normalised using ...
user avatar
  • 183
1 vote
0 answers
24 views

Recurrent neural Network for survival analyses: Dealing with forecast data as feature which can exceed the number of days untill a event occurs

I am building a Recurrent Neural network (LSTM) for predicting the number of days until a Pollen season starts (when the cumulative of the year exceeds X). One of the features I am including in my ...
user avatar
1 vote
0 answers
42 views

How to exploit translational symmetry for extrapolation in video generation using machine learning

I'll try to rephrase my problem in the context of video processing. Imagine that initial frame of video has some translational symmetry. The frame evolves according to an update rule. I generate a ...
user avatar
1 vote
2 answers
360 views

How do we choose the activation function for each hidden node? [duplicate]

I am new to neural networks. I would like to use them as a fitting or forecasting method. A simple NN model that does not contain hidden layers, that is, the input nodes are directly connected to the ...
user avatar
  • 111
5 votes
1 answer
107 views

What approach should I take to model forecasting problem in machine learning?

I have a dataset which contains 4000k rows and 6 columns. The goal is to predict travel time demand of a taxi. I have read many articles regarding how to approach the problem. So, every writer tell ...
user avatar
4 votes
2 answers
131 views

Why is it harder to achieve good results using neural network based algorithms for multi step time series forecasting?

There are different kinds of machine learning algorithms, both univariate and multivariate, that are used for time series forecasting: for example ARIMA, VAR or AR. Why is it harder (compared to ...
user avatar
  • 183
1 vote
0 answers
29 views

What Model Used for Forecasting Sales with Dynamic Holiday

I'm working on a project where I need to forecast sales data where I have history of 1 year (2017) daily data. I am new on Artificial Intelligence topic and after searching for a while, I think ARIMA ...
user avatar
1 vote
2 answers
262 views

predict customer visit

Suppose we have a data set consists of columns TransactionId, CardNo, TransactionDate then how can we calculate the customer purchase interval (means if customer A purchased on Jan 1st and after ...
user avatar
1 vote
2 answers
301 views

For forecasting and trading control, given limited data, what AI approaches are well matched?

I'm working on stock price prediction and automatic or semi-automatic control of trading. The price trends of these stocks exhibit recurring patterns that may be exploited. My dataset is currently ...
user avatar
  • 207