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Honestly it seems you are quite far from what would need a supervised vision approach. I suggest you to try a simple non-ML approach first : extract text with a standard library then just label what would count as a 'bullet' then check if there is more than one in a row. This might just work and if it doesn't it will help you understand why. Going the whole ...


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If i understand you right what you trying to achieved is Text Classification using Context Information.Also i assume that you have target column ,so you need to used supervised learning(please correct me if my assumptions are wrong :) ) For such cases the best is to use recurrent neural networks like LSTM for example.Please check this https://www.kaggle.com/...


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What you are looking for is training a classifier with data augmentation. In the context of image classification, this may refer to changing the pose of the object by skewing or rotating the image. In the context of text classification, this can be imagined as classifying different versions of the same sentence with an alternating word sequence (some ...


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There are two ways to setup prediction depending on your dataset: If you have a row every minute/hour/day just find the rows with the right alarm and put 1 the observation 1/7/10 days ahead depending how much ahead of time you want to predict the alarm. Of course the less recent it is the more difficult to predict. If you have a row every event it is harder ...


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The first step is to pick a target variable. What are you trying to predict "problem name", "severity", "sub category", …? Based on the target, it becomes either a conventional regression, ordinal regression, binary classification, multi-class classification, or multi-label classification problem. Another issue might be how the ...


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As explained in the link you provided, you will encouter problems with monthly data when you try to see what happen at a timestep below. They do not exactly use the same graph as yours but they say that : "The seasonality has low uncertainty at the start of each month where there are data points". It seems to be what happen here : on your graph you ...


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The real state of the art here is the Matrix Profile suite, developed by Eamonn Keogh and his team in University of California at Riverside (UCR). Here are some links to get you started: Matrix Profile Foundation homepage The UCR Matrix Profile Page MPA: a novel cross-language API for time series analysis paper (2020) with links to Python, R, and Go ...


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From Keras docs: https://keras.io/api/layers/core_layers/dense/ Input shape N-D tensor with shape: (batch_size, ..., input_dim). The most common situation would be a 2D input with shape (batch_size, input_dim). Output shape N-D tensor with shape: (batch_size, ..., units). For instance, for a 2D input with shape (batch_size, input_dim), the output would have ...


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I faced a similar problem and the quick solution I came up with was iterate through the main data set, create an instance of TimeseriesGenerator for each of the subsets and feed it to the model. So in your case that would be: go through the collection of articles, instantiate TimeseriesGenerator for each article and feed the windowed samples to your model, ...


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The computational complexity of simple single-layer recurrent networks, either vanilla RNNs, LSTMs or GRUs is linear with the length of the input sequence, both at training time and inference time, so $O(n)$, where $n$ is the length of the input sequence. This is because in order to get the last time step output, you need to compute all the previous ones. ...


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I am assuming you have a model running in production and it retrains periodically (example: every month) and it forecasts for the next X days (example: 30 days) and you are trying to evaluate the model using RMSE and MAPE (If this assumption is wrong, please clarify it in your question) If I use past 1 year data for training and forecast for next 30 days ...


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Doing some further searching, I found a nice post by Jason Brownlee that has helped me better understand the problem and one potential solution: a multi-input model with an LSTM input for the historical data and a vector input for expected conditions. Furthermore, Keras' Functional API (https://keras.io/guides/functional_api/) will help me build such a ...


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the syntax arr[:,:-1] selects all rows and every column except the last one. Python can use negative indexing, but it's inclusive-exclusive such as [a,b): inclusive of a, exclusive of b. If you don't use the : operator, such as arr[:,-1], then it simply selects the entire last column. So in the context of your example, the last column is the value to be ...


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Well ... I would do exactly what you did. The derivative on original signal is very noisy. I would probably take derivative out of moving-averaged smoothed signal, however it brings some delay into your detection. See this answer for more info and python code. The other approach is to detect the point in time-frequency domain. Simply plot the STFT of your ...


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If an independent variable (x) has a lagged effect on dependent variable (y) of a OLS regression model, you must insert its lagged value and not current value in time series data. Your proposed stats model includes both current value and lagged value . This is not justifiable. Therefore, correct your model and proceed.


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ARIMA should work fine - I'd recommend the auto-arima package here: https://pypi.org/project/pmdarima/ Alternatively, if you're happy with it to be a little more opaque, you could use Facebook's Prophet. Generates some really accurate predictions with very little tuning.


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ARIMA could work, I think it's the right approach. It's simple enough to be used on a small dataset, but sufficiently flexible at the same time. If you are using Python, library statsmodels allows you to implement ARIMA regressions. You have to grid search and find the right parameters to find the best fit, and run the prediction. If you want to know how to ...


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Using a relu function at the n-1 layer could be too constraining if you want your network to produce both positive and negative values. I am not sure about your image preprocessing, but I would first give a try to change (at least) the last activation function relu to leaky relu or tanh (an activation function that produce both positive and negative values). ...


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