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Welcome to the site. I think you were right that the prediction lags behind the true value because the series is autoregressive (i.e. a strong way to predict tomorrow’s value is “It will be about the same as today”). Your model therefore corrects itself with the new information when it misses a big jump. In other words, if the price jumps one day and your ...


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Moving averages will give you a smoother time series so that a trend is easier to see by eye. This approach makes sense when you’re exploring the data. The next step is to try to comment on where the time series will go next. Based on the tags you have chosen for your question, you are comfortable writing python. You might consider Facebook’s open source ...


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Yes it makes sense, a moving average makes the curve "smoother" in the sense that it's less sensitive to short variations. This usually makes it easier to observe the general tendency. You could also try different time periods for the average, e.g. 10 days or 15 days. It looks to me like there's a moderate increase trend in your data, but the ...


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Welcome to Data Science on Stack Exchange, This is a common question, predicting future prices or forecasting. The gap you see is due to the random nature of prices such as this, along with the underlying complexity of this topic. Unless there is a time pattern in the data, a LSTM model won't predict well. LSTM will especially perform poorly if the data is ...


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Is the term you're looking for simply the rate law of the reaction? Every chemical reaction has an associated kinetic rate law, consisting of its rate constant, and its reaction orders. Consider the reaction: $A + B -> C$ Due to the conservation of mass, we can effectively model the rate of formation of the product C, as the rate of consumption of ...


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X[train].shape[0] - This is the number of instances. Let's say it is M X[train].shape[1] - This is the shape of each instance. Each instance is (1 x N) Since input instances are of 1-D, the input data become m x N. Had it been 2-D, it would have been m x Nx x Ny And one more question, I know that CNN required fixed input size. But I split my data into ...


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The inputs of a CNN must have the same shape during prediction as when it was trained. So if you have a CNN trained on 50 time-steps windows, then you can make predictions on a stream of input by updating the data in this window continuously. Each new time step you push the most recent row onto the end, and drop the earliest row. Of course it is possible to ...


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Typically in a Conv1D layer for a time series, the features can be different measurements taken or recorded at the same time period. So they can be related to each other. For example, if you are trying to predict time=4 below, the question is whether there is a relationship between your features, meas1 and meas2? If so, you want to keep the features together ...


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This is a supervised learning problem, specifically regression. Supervised learning refers to using measured values to model and predict output values. The regression part means that you have numbers as your response variable, as opposed to categories. (Something called ordinal regression blurts the distinction between, but I don’t think you’re in an ordinal ...


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ARIMA models try to capture autocorrelation in your time series in order to make predictions. It is composed of three main models: AR, MA and I. So, you build your model to predict future values based on a linear combination of past values, linear combination of past errors and a differencing term (I) that accounts basically for a trend. You should get more ...


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ATM I know of TSimulus and TimeSynth to generate data programatically in a controlled manner (instead of generating random data). TSimulus allows to generate data via various generators. TimeSynth is capable of generating signal types Harmonic functions(sin, cos or custom functions) Gaussian processes with different kernels Constant Squared ...


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