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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1answer
44 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 ...
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0answers
30 views

Time series forecast for everyday for till a distant future

I have time series data for every single day from last 5 years with seasonal variation and a general increase in trend. This is what my data looks like: And I am trying to predict for every single ...
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0answers
25 views

How to train an Encoder-Decoder LSTM for sequence to sequence prediction?

I have a dataset where for each country there is a name (string) and a multivariate time series (all integers). I am trying to use an Encoder-Decoder LSTM to forecast the next time steps in the time ...
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0answers
22 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 ...
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0answers
19 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 ...
2
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3answers
133 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 ...
1
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0answers
21 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 ...
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0answers
36 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 ...
2
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0answers
36 views

How can I prevent a Recursive Neural Network from performing extremely poorly after a few cycles?

I've trained a neural network that can predict the $(n+1)^{th}$ element in a sequence, given the $n^{th}$ element. It does a pretty good job doing this, with very little error. The problem emerges ...
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2answers
148 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 ...
5
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1answer
96 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 ...
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2answers
104 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 ...
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0answers
26 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 ...
2
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2answers
165 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 ...
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2answers
222 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 ...