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
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 ...
4 votes
2 answers
136 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 ...
  • 183
2 votes
3 answers
216 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 ...
  • 183
2 votes
1 answer
73 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 ...
1 vote
2 answers
280 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 ...
1 vote
2 answers
316 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 ...
  • 207
1 vote
2 answers
369 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 ...
  • 111
1 vote
1 answer
126 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 ...
1 vote
1 answer
47 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, ...
1 vote
1 answer
83 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....
  • 11
1 vote
0 answers
49 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 ...
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 ...
  • 11
1 vote
1 answer
81 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 ...
  • 119
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 ...
  • 111
1 vote
0 answers
45 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 ...
  • 1,195
1 vote
0 answers
25 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 ...
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 ...
1 vote
0 answers
30 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 ...
0 votes
0 answers
18 views

Multi-Variate Time-Series forecasting with XGBoost

I have trained an XGBoost model on a time-series dataset for predicting a value. The time series has 5 features and one label (the target value). The trained model works fine on both training and ...
  • 181
0 votes
0 answers
11 views

How to investigate annual time series data?

I have annual time series data from 2000 to 2020. The brand has introduced new marketing camping in 2010 and I want to investigate the impact of this policy, that's why I am trying to explore the ...
0 votes
0 answers
10 views

Best way to incorporate lat and long-itude into demand forecasting

Suppose I need to create a demand forecast for every hour for every zone of a city. A zone is a composed of an latitude and longitude interval and we can assume it is square (for example a zone is 1 ...
  • 143
0 votes
0 answers
12 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 ...
0 votes
0 answers
36 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: ...
0 votes
1 answer
37 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 ...
0 votes
0 answers
59 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 ...
  • 1