deep learning for text and sequences on this plotyou can see daily periodicityespecially evident for the last days also note that this -day period must be coming from fairly cold winter month if you were trying to predict average temperature for the next month given few months of past datathe problem would be easydue to the reliable year-scale periodicity of the data but looking at the data over scale of daysthe temperature looks lot more chaotic is this timeseries predictable at daily scalelet' find out preparing the data the exact formulation of the problem will be as followsgiven data going as far back as lookback timesteps ( timestep is minutesand sampled every steps timestepscan you predict the temperature in delay timestepsyou'll use the following parameter valueslookback --observations will go back days steps --observations will be sampled at one data point per hour delay --targets will be hours in the future to get startedyou need to do two thingspreprocess the data to format neural network can ingest this is easythe data is already numericalso you don' need to do any vectorization but each timeseries in the data is on different scale (for exampletemperature is typically between - and + but atmospheric pressuremeasured in mbaris around , you'll normalize each timeseries independently so that they all take small values on similar scale write python generator that takes the current array of float data and yields batches of data from the recent pastalong with target temperature in the future because the samples in the dataset are highly redundant (sample and sample will have most of their timesteps in common)it would be wasteful to explicitly allocate every sample insteadyou'll generate the samples on the fly using the original data you'll preprocess the data by subtracting the mean of each timeseries and dividing by the standard deviation you're going to use the first , timesteps as training dataso compute the mean and standard deviation only on this fraction of the data listing normalizing he dat mean float_data[: mean(axis= float_data -mean std float_data[: std(axis= float_data /std listing shows the data generator you'll use it yields tuple (samplestargets)where samples is one batch of input data and targets is the corresponding array of target temperatures it takes the following argumentslicensed to