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You can't meaningfully talk about DNNs or ARIMA being "better at time series forecasting". It depends enormously on what kind of series you are looking at: short vs. long series, many vs. few or only one related series, causal drivers or not etc. Anyone who makes sweeping statements here is like a salesman who knows exactly what kind of car you need - ...


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Let's start with a generalization. Suppose the original data are a series of values $\mathbf{x}=(x_1, x_2, \ldots, x_n)$ associated with times $\mathbf{t}=(t_1, t_2, \ldots, t_n).$ You have in mind a new set of ordered times indexed by the integers $\mathbf Z$ along with a function $f(k, \mathbf{t}, \mathbf{x})$ that determines what new value to assign to ...


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You question is almost meaningless without showing/explaining the data. The tells you want invarinces you need (phase. offset, amplitude, uniform scaling, warping, complexity etc [a]). But see for example [b] [a] https://www.cs.ucr.edu/~eamonn/Complexity-Invariant%20Distance%20Measure.pdf [b] https://www.cs.ucr.edu/~eamonn/time_series_bitmaps.pdf


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THE BIG PRINT: Since the ACF is dominant i.e.has the most significant values the process is autoregressive (AR) . The order of the AR model is determined by the # of significant values in the subordinate structure ... in this case the PACF .. thus 2. The answer is (2,0,0) THE LITTLE PRINT: When does this simple strategy work ... 1) When there are no ...


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There are really 3 cases one might consider: (1) the longer-term gradients vanish (2) they explode (3) they neither vanish nor explode somehow. The problem with case (1) is that shorter-term gradients, being added to longer-term gradients, then dominate exponentially the longer-term ones, making it difficult to learn long-term dependencies. Furthermore, in ...


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Granted that a single observation may not tell you everything you'd like to know, but continuing from @AdamO's Comment, information from one occurrence may be useful. Now you know that overheating can happen. You might assume that such events happen according to a Poisson process with rate $\lambda$ so that times between events are exponential with rate $\...


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Your reviewer is (maybe) right, but he/she kind of cherrypicked his comparison point. First, why a maybe. Even if two data points are in the 95% interval of each other, their difference might be significantly different at a 5% margin. See: https://towardsdatascience.com/why-overlapping-confidence-intervals-mean-nothing-about-statistical-significance-...


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DTW does not have a notion of "convergence", as it is not an iterative optimization procedure. When DTW is too slow, and doesn't finish in acceptable time, the obviously next best idea is to use bounded DTW. Full DTW can take O(nm) time of n and m are the lengths of your series (at least if implemented well...). You can bound this to O(nk) for a fixed ...


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This fall into the realm of time-varying covariates, and what you describe is the functional form that is indeed of importance. Based on the description of your problem I would say that the cumulative effect of calcium intake could be a candidate functional form. Another aspect that you will also need to consider is whether the time-varying covariate is ...


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Adding to the first comment, I would recommend you to have it as channels. i.e. whole data becomes 3 dimensional, which can be still processed by deep learning models. Let me put it clear, Dimension 1 - rows Dimension 2 - Sensors Dimension 3 - Location so your data will look something like this [[[38, 38, 35, 33, 32], [18, 18, 12, 11, 09]], [[33, ...


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RNNs are most commonly used as density models over some space of sequences. To be more precise, if we have some sequence $X = x_1, x_2, \ldots, x_k$ then our model describes the distribution $$P(X) = \prod_{i=1}^k p(x_i | x_{<i})$$ (which is valid by the product rule). More specifically, our RNN models each conditional term $p(x_i | x_{<i})$ as $x_i \...


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You might also consider the drivers behind 'Deep-learning-beats-all' trend you mention. Much of the hype around these techniques comes from the superiority of these methods in image recognition and natural language problems. These domains are defined by exceptionally large datasets (e.g. ImageNet > 14 million images, it's possible to find very large text ...


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Statitical tools applied to time series forecasting are very developed and approach-oriented methods. You find many technics arima sarima sarimax var varimax vecm.... And each method had been developed for a particular situation and type of data and serie. In the other hand DNN such as RNN, LSTM.. Are challenging models that have not been very used in this ...


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A time series can have 1) short term arima structure ; 2) seasonal arima structure ; 3) seasonal deterministic structure ; 4) one or more level/step shifts ; 5) one or more deternnistic trends ; 6) one or more pulses ; 7) changes in model parameters over time ; 8) changes in model error variance over time. Are these the kinds of inferential features that ...


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I'd recommend creating a dataset that contains one record per student. For each record, you have the target variable (aka the label): 1/0 to indicate whether they responded 'Yes' or 'No' to the survey question. Then you can do some feature negineering to create attributes for each student. The model would take the following form: $$ logit\ E(Y_i)=\alpha+\...


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