Why the LSTM prediction value gives the randomize number ? From that how to take decision.
Scenario is: I want to predict in future power cut in house holds. I have a past 3 month hour based energy meter reading. I trained and LSTM model gives randomized number in next 24 hr..
How do I conclude which hour power On/Off?
Sample output shown here:
Related
I'm a beginner and trying to use Azure Machine Learning Studio to run a forecasting task automatically, but I'm very confused about how to determine the parameter - Forecast Horizon.
I have found some explanations on the official website.
forecast horizon: Indicate how many time units (minutes/hours/days/weeks/months/years) will the model be able to predict to the future. The further the model is required to predict into the future, the less accurate it becomes.
My data increments each hour and I want to predict the values of the next 24 hours one time (namely, multistep-ahead forecasting). Should I set Forecast Horizon to be 24?
In addition, I have run the forecasting experiments twice with the same settings except for Forecast Horizon, in which one is set to be 1 and the other is 24. I expected the predictions are more accurate with Forecast Horizon = 1 (can I understand it with one-step forecasting?), but it's worse, therefore I doubt my understanding of Forecast Horizon is wrong.
Here are images of my prediction results with different forecast horizons:
The test set includes all data of a year (24*365 points).
Starting with Forecasting tasks, it require the time_column_name and forecast_horizon parameters to configure your experiment.
Forecast Horizon is the number of periods forward you want to forecast. The horizon is measured in time series frequency units. The forecaster should predict out units based on the time period of your training data, such as monthly or weekly.
In your case you can set your Frequency to hourly forecast and Forecast horizon to 24.
REFERENCES:
Forecast demand with no-code automated machine learning
I am working on a bitcoin price predictor, and I realize that it's non sense to predict an exact price at a given time.
What we want when predicting some currency price can be summarize with this question: "What is the probability for the price to reach value X in a specific time range ?"
I have hard time to integrate this thinking into a RNN /LSTM architecture. My first thought was to build a custom Loss function that compare the output of the RNN (typically, a predicted price) with the real lower and upper price of the next day, then if the lower_price < predicted_value < upper_price the RNN output should be "classified" as correct (loss = 0), otherwise the loss would be > 0. But I am sure there already exists a better solution for this kind of problem.
Any idea ?
Thank you
There are a number of different ways to do what you are asking. However I think what you are looking for is a quantile loss function.
def tilted_loss(q,y,f):
e = (y-f)
return K.mean(K.maximum(q*e, (q-1)*e), axis=-1)
Notebook with full source code. Or if you prefer PyTorch you can find an implementation here.
How to train LSTM model on multiple time series data where each time series has different timesteps/lags.
Use case: I have daily transactions(WIthdrawals) of 100 ATMS for last 5 years. Need to forecast next withdrawals based on timesteps for each ATM.
How to use embedding(latent representation for each ATM ID) if I pass ATM ID as feature and train on all data at once
How to pass all time series data to LSTM at once, how LSTM will distinguish one time series from another?
I have a question about how to calculate features for future time frames. Consider the below dataset and consider today is: 2019-11-11. I have last 2 years of daily data and below is last 6 rows:
Date, Temperature, Sales
2019-11-06, 25.5, 500000
2019-11-07, 24.2, 550000
2019-11-08, 25.1, 560000
2019-11-09, 22.6, 510000
2019-11-10, 22.3, 520000
2019-11-11, 24.4, 535000
Now I have to predict Sales for 2019-11-12, 2019-11-13, 2019-11-14. In order to predict sales for those dates, I have to provide below test data to the machine learning trained model:
Date, Temperature
2019-11-12, temperatureX
2019-11-13, temperatureY
2019-11-14, temperatureZ
What will be values for temperatureX, temperatureY and temperatureZ since these values will be coming from future as well?
There are different solutions.
I suggest you start with Time-series Forecasting using Azure AutoML or to dig deeper Auto-train a time-series forecast model
If you need an interpretable model you could train a Linear Model (LM) or an other regression model in R or Python. It might make sense to derive some features from the date such as month, day of month, season or so. This approach has the benefit that you can calculate the confidence interval.
As it is a multivariate time series, also have a look at Vector Auto Regression, see A Multivariate Time Series Guide to Forecasting and Modeling (with Python codes)
If you are just interested in predicting values, you can try a recurrent neural network or LSTM. See GitHub Azure/DeepLearningForTimeSeriesForecasting
Easy answer? You can't predict if you don't have the independent variables that explain your target at prediction time.
That being said, you can usually get a weather forecast for at least a week ahead of any date through a simple web search. So if you don't require a very large max horizon, you can use predicted weather forecasts for your temperature values (x, y, and z). Your retraining period would then become weekly, or however far out you're able to find existing weather forecasts.
Ref: https://datascience.stackexchange.com/questions/27171/what-to-give-as-predictors-to-predict-future-values
I have a time series dataset of customer behavior. For each month, I have one row per customer which includes a set of features(for example, the amount of spending, number of visits, etc.) and a target value(a binary value; does the customer buy product "A" or not?).
My problem is: I want to train an LSTM model to predict the target value for the next month(does the customer buy product "A" next month?). Since I have multiple time series(one per customer), I have more than one sample per timestamp(for example, for January 2010, I have more than 1000 samples, and so on). How do I train the model? Do I go epoch by epoch and for each epoch, fit the model one by one on all customers? Is there another side to this I'm missing?
Dataset features:
Number of customers: 1500;
Length of time series: 120;
Number of features per customer: 80(before adding time-shifted features);