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Questions tagged [multivariate-time-series]

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Can we ignore time fixed effect for panels with long time series?

I have a panel data comprising of 50 companies and 5000 observations for each. I am trying to conduct a fixed effect panel regression. I am able to run one-way fixed effect model in R, but I am unable to conduct two-way fixed effect or time fixed…
Arunava Bandyopadhyay
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Accounting for repeated measures in pairwise.adonis2()

I have a dataset where several replicate sites assigned to either a treatment or control group were sampled before, during, and after a heatwave. I want to know whether the treatment buffered ecological community changes before-to-during or…
Joshua Smith
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How to create a single dataframe using TimeSeriesDataSet() to feed a TemporalFusionTransformer model?

I am trying to use pytorch_forecasting TemporalFusionTransformer to predict oceanic environmental variables. My data is organized as follows: Location (sensor's locations): loc_a loc_b loc_c Ocean environment features/timeseries related to…
jeffialho
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dealing with non stationary data for a forecasting

I have a dataset where i have a column name week which has week numbers infor (1,2,3....145). So the forecasting should be done for week 146 to 155 that is next ten weeks. Which machine learning algorithm should I use as time series with Arima or…
Karishma Manohar
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How to upload stock price csv in a time series format in R?

I am trying to run a multivariate garch model in R. I have my own csv file which contains returns of stock, bond, hold along with the dates (2018-01-01 to 2022-12-30). But the problem is I am not able to upload my csv in a time series format like…
Abishek S
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How to develop a forecasting algorithm that predicts the heat demand of building for the next week on an hourly basis in Python 3.9

Input data: 3 normalised heat demand hourly profiles (averaged time series) of generic buildings for 2021 and related outside temperature (in 0.1 °C) Actual heat demand in kWh of HYLIFE OFFICE building over past year 2021 and first month of…
Vsdm in
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Windowing on LSTM autoencoder python

I have training dataset of length 8000 with 4 features. With Window size of 5, I have output of LSTM autoencoder in the shape of (7996,5,4). But I required in the shape of (7996,4) to compare with input what I can do? Actually I want to plot MAE…
Vikas Goyal
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Synthesizing Multivariate/Panel Timeseries data with LSTM

I have a multivariate time series that tracks material sales over time. The most peculiar column is sales because it has an intermittent pattern (i.e. many 0 values over time) material prices date sales 0 2 1.41…
SaltySenator
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Is there a way to predict time-series data in deep learning you give input features like in machine learning during model.predict()?

I have to predict the Air Quality Index for next 15 days, dataset is as follows: Dataset with location But there are 6 different locations in the data set, and i want the model to show the results based on the location given in model.predict()…
Pratham Soni
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Regression : how to handle multiple multivariate timeseries?

I am trying to develop a model using machine learning that reproduces a biological behavior. My goal is to do a regression of timeseries e.g from multiple input each time_step predict multiple output and not forcasting. For this, I have : as input:…
Ketchup
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Time series fitting: use of 'trend' in VAR models via statsmodels.tsa.vector_ar.var_model.VAR

I'm using the statsmodels.tsa.vector_ar.var_model.VAR package to fit some bivariate time series. That is, time series of vectors with 2 components: (x1, y1)T, ..., (xN, yN)T. However, I do not understand how to use the parameter trend in the…
Francesco
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Mutli variate time series prediction - Conv1D and loss function - pytorch

I have a couple of questions. I have data of the following shape: (32, 64, 11) where 32 is the batch size, 64 is a sequence length and 11 is the number of features. each sample of mine is 64X11, and has a label of 0 or 1. I’d like to predict when…
Tom
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Fast way to find k nearest neighbors from only past, in R?

Assume I have a numeric data with 10,000 rows and 8 columns. I want to obtain the first k neighbors for each row (skipping first 1,000 rows) using euclidean distance but the catch is for each row I am only interested in the previous rows. (e.g. for…
cccanhakan
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How to use a multidimensional array (3D or 4D) for time series classification

I just can't wrap my head around this so I decided to ask. Imagine that you have a 3D array with 6 layers. Each of these layers contain data about 6 people demonstrating 6 different movements 10 times. Each movement varies in length, the longest…
raurackl
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Exploding gradient Problem with LSTM model build with LSTMCell (PyTorch Implementation)

I'm tried to solve timeseries prediction. Where my input is multivariate. My input has 4 variable, and my target is another variable. I've processed the data as following. 4 variables and 60 timesteps input sequence. So, each input shape is (1,…
carl
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