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.
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Architecture of an LSTM with multiple (dependent?) time series
If you have multiple time series data for a given problem (e.g predicting house prices and data is available per city).
Per city there is a list of features and the target feature.
If you want to ...
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HuggingFace : Expert Model for Weather Forecasting Ensembles
I'm looking into recent Weather Forecasting Models like FourcastNet, GraphCast, Pangu Weather etc. For weather forecasting, I found Encoder-Decoder architecture the predominantly used design. But can ...
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Preparation of multivariate time series data
I am doing a university project on index/stock price prediction.
I plan to use a combined cnn-lstm model, and I have several different types of data: Open High Low Close Volume, values, fundamental ...
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How to train continuous probability distributions as output from a neural network?
I'm training time series models on numerical forecasting, and I'm seeing inherent difficulty in modeling the uncertainty of the values. Time series forecasting generally has a pattern of uncertainty ...
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Predicition of future batches in time series
i am working with neural networks and i want to predict the time series further ahead. I did a course on neural networks where this kind of problem is faced. But i dont really understand how it works.
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Executing Multiple ML Models simultaneously on multiple cores to reduce the model building time
I have a time series forecasting problem which consist of date, item no and quantity columns. I have defined a function which takes input as data frame and forecasting period (Daily,Weekly,Monthly,...
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What Kind of Models and Loss Functions for User Churn Prevention by Promo Codes?
The Company Business Model
Bike rental with an app, where riders pay for the time they rented the bikes for.
The Business Case
User (rider) attrition prediction, and ideally, prevention. Basically, ...
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Simultaneous forecasting and classification
I'm working on a project where I need to perform both forecasting (regression) and classification using time series data. The dataset is labelled.
I've been exploring LSTM networks due to their ...
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What are the most common fault prediction algorithms?
I have to predict a fault (automotive related) as much in advance as possible.
Right now I have found a solution that is somewhat satisfactory (a good number of true positives and a low number of ...
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Should we consider the prototypical forecasting task as self-supervised learning?
In NLP, the task of "predicting the next word" is an example of self-supervised learning. An essential part is that the label can be computed programmaticaly and does not require explicit ...
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Are there public examples of AI models that predicted short-term price well?
This question is inspired by Is there any AI model that predicts short-term stock price well?.
As answered, game theoretically either nobody would reveal such a model or it would have been alread ...
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Is there any AI model that predicts short-term stock price well?
One simple way of predicating short-term stock price is to simply take the average price of the previous few days.
I wonder if there is some AI model/methods which can do better than this using only ...
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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 ...
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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....
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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: ...
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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
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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 ...
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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, ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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