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0 votes

How to perform Timeseries Forecasting on dataset with repeating dates?

Did you get the solution for this? I am facing same issue
1 vote

Python (S)ARIMA models completely wrong

You did not calculate the uncertainty in your models. Typically in forecasts uncertainty grows exponentially. There is a reason you can't predict daily weather a year in advance.
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3 votes

Python (S)ARIMA models completely wrong

Before testing on 1200+ timesteps, you should test on 100 or 200 timesteps (with sampling or deducing data), because ARIMA have logical constraints. Then, I suggest to use a simpler model of ARIMA ...
5 votes

Python (S)ARIMA models completely wrong

I looks like youe have two observations per day, is that correct? Before using an ARIMA model with seasonality=7 (weekly seasonality) you need to transform your time series in a way that you only have ...
0 votes

Unsupervised learning for anomaly detection

You can consider this package, pyod. It has various anomaly/outlier detection algorithms all in one package. Approaches : Use a dimension reduction technique like tsne, which can collapse high D into ...
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0 votes
Accepted

How does one perform a Canova-Hansen test in Python?

In general this could be achieved with the snippet below where you have to replace x with your observations. However I'm not sure whether the Caonva-Hansen-Test is suitable for weekly observations (m=...
1 vote

Visualize time series data

Color line for each ID differently . The gaps in the data should show up in the graphs as-is . ...
1 vote
Accepted

clustering time series with different sized time series

Try using the to_time_series_dataset function in the tslearn.utils module. This takes a list of lists as input and returns the data formatted as a numpy array, e.g.: ...
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1 vote

Smooth Ternary (binary but 3) Time Series Data

Use multiple outputs (multi-label classification) instead of a single output. Then the output will encode the models estimated probability for that particular class. For example: (0.9, 0.1, 0.1). Then ...
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1 vote

Explanation for a parameter in ADTK package

It is used in combination with the range of historical interquartile values to determine if a value is anomalous or normal. The range of normal values is defined as follows: $[Q_1 - c * IQR, Q_3 + c * ...
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1 vote

predictions based on irregular repeated measures?

Time-series models can reach very good results as long as there is enough data. If you don't have enough data for one person and if this data is irregular, there are good chances that no time-series ...
0 votes

Taxonomy of train-test split approaches

I don't know if there are many train/test split solutions, but it mainly depends on 3 things: The data quantity The data complexity/balance The data type Multi-class or single class To answer your ...
0 votes

Time Series with Exogenous Variables

In 2022, you can also use NBEATS model and Tranformer based models. It could be easily used with the help of darts library. https://pypi.org/project/darts/
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1 vote
Accepted

Detrend a time series

Yes the author has made a mistake. The trend needs to be divided from the multiplicative time series while de-trending it. In the next code section he has de-seasonalized correctly: ...
1 vote

XGBoost training on sample of time series data

More generally seen, your problem is, that you want to predict a multi-variate / multi-dimensioan lable, but your algorithm only supports uni-variate / 1-dimensional target variables. I see two direct ...
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0 votes

Is removing huge data for time series model right?

The short answer is yes. For the long answer, you should read this article, which I have also read it recently (thanks to other users here).
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2 votes

Detect data (web textual content) age

Go digging through the html - you will find consistent tags/styles/formats across multiple sites. For example: From the bbc we can see a datetime/...
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0 votes
Accepted

Detect max extreme peaks/valleys with min 5% vertical delta

The following zigzag python library provides this ability. https://github.com/jbn/ZigZag/
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0 votes

Is there an R tutorial of using LSTM for multivariate time series forecasting?

I'm dealing with the same issue. I know this post is pretty old but I have found some recent posts that may help: there is a 3-part series on this subject in R studio AI blogs. https://blogs.rstudio....
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0 votes

Sorting column in dataframe in each group in R

order() is a way to sort data frames, that should have worked. Consider sharing the code used--perhaps it is just a usage issue. Another good way to sort if you're open to using to using data.table is ...
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