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1 vote

Which dataset for multivariate time series forecasting

This is the typical problem where you have to really model the phenomenon. You are supposed to use a pencil and paper and actually do some choices. One thing you can do is to create a function with an ...
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Time Series Clustering on sales data -- any ideas?

Time series could be applied for sales in various different fields. Here a quite extended view. There is the ones you've mentionned: What sales trends there are across time. What products customers ...
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1 vote

Applying Differencing on a time series, before or after train and test split?

According to econometrics literature, the standard approach is to convert your data into log returns as follows: $r'(t) = log(P{t} / P_{t-1})$, where $P(t)$ is the price at timestep $t$. This improves ...
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-2 votes

Time series prediction using ARIMA vs LSTM

I think, you are misusing MachineLearning & Deep Learning when trying to predict tomorrow... - any statistical Approximations of the chaos can show just averaged Tendency & its borders (...
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Time series prediction using ARIMA vs LSTM

ARIMA gives Trend (or Regression) - can see its slope (that reflects speed of change dx/dy)... LSTM gives MovingAverage - posessing curvature & slope in each moment - that can characterize not ...
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LSTM to multivariate sequence classification

In addition, this article explains some generalized methods for clustering using DTW on multivariate data. Especially useful for IOT data https://link.springer.com/article/10.1007/s10618-020-00727-3
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1 vote
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Random kernels in multivariate Rocket sktime

Looking at the source code, the sktime implementation randomly selects the number of features to use for each kernel and then randomly chooses the input features. For example, if your dataset contains ...
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1 vote
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RNN/LSTM timeseries, with fixed attributes per run

You can create a sort of encoder-decoder network with two different inputs. ...
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0 votes

ARIMA forecast for timeseries is one step ahead

It appears you are using Python's statsmodel ARIMA. There are two options: Change order= agrument. The p, ...
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Is there a way to forecast a time series multiple linear regression using externally made dummy variables?

If you use exogenous variables, then you need to provide their future values in the forecast() function. You only provide the value of ...
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1 vote
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Why fourier transform extrapolation goes to extreme on edges but not in the middle, how to fix it

This happens because the FFT assumes your signal is periodic. This is why you see the reconstruction increase on the left and decrease on the right. You can avoid this by artificially making your ...
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1 vote

Many regression lines in a plot

I don't know the dataset but normally this would be done much more easily using the color dimension, something like this: ...
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1 vote

Removing seasonality in time series forecasting

Removing seasonality is not something you are obliged to do. It really depends on the model. The idea of decomposing time series (you are not actually removing seasonality, it is simply a component ...
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Plot multiple time series from single dataframe

Not sure if you want to have this done using just pandas/matplotlib, but this can be done relatively easily using the ...
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Clustering time series data using dynamic time warping

Dynamic time warping (DTW) is for temporal alignments. You are comparing non-temporal alignment by adding a constant between the two time series. Here is an example of temporal alignment by shifting 1 ...
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How can we predict a value after several rows of data?

Try to make all the rows of the week one row. Considering the max number of rows/weeks: Week 1; $x_1, x_2, x_3...x_{90}, v_1, v_2, v_3...v_{90}...z_1, z_2, z_3...z_{90}; y_1$ Week 2; $x_1, x_2, x_3......
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