I am new to LSTM. My data contain 1381 data point and one column. Train set=1104 and Test set=277. I used LSTM to predict the values. So I had given past data points as 120, layer=2, and epoch=10. After predicting the values, I plotted the validation and training, but my plot looked like this.
Anyone, please help me? Why does the plot look like this?
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I have ~2.6K hours of sales data with a positive linear trend as well as daily and weekly seasonality. See plotted data. I have tried to model the data using SARIMA and TBATS in python. In both cases, I cannot get the predictions to work as I intend.
For SARIMA, the in sample predictions look great, but when I try to forecast into the future, it looks completely wrong.See here for in sample SARIMA predictionsSee here for how poor the out of sample SARIMA predictions are.
For TBATS, the predicted values match the daily and weekly pattern, but is missing the positive trend, despite me forcing so that use_trend = True. See TBATS model prediction here
I have no idea what I'm doing wrong and have been stuck on this for days! Any advice greatly appreciated.
I am new to machine learning and therefore, trying to figure out if my dataset is enough to run LSTM model.
I am trying to do time series forecasting on daily road traffic data. Currently, I have daily data (2012-2019) for 20 different locations. Essentially, I just have ~2800 data points for each of the location. Is that a good dataset to start with?
Any recommendations on how I can tweak the data or transform it to help with my dataset?
Please help! Thank you!!
Consider this your dataset is ~ 2800*20 examples. Now you can always run an LSTM/RNN model on this much data, but you should try to check whether they are outperforming baseline models like Autoregressive Moving Average (ARMA),
Autoregressive Integrated Moving Average (ARIMA).
Also, if data is in format:
Example_1: Day_1: x, Day_2:y, ...., Day_n: xx .etc
Rather than inputing whole Day_1 ... Day_n features to predict Day_n+1
You can always increase your dataset by using Day_1 to predict Day_2 and so on.
Check this LINK. Something I worked on which might help.
I collected ~1500 labelled data and trained with yolo v3, got a training loss of ~10, validation loss ~ 16. Obviously we can use real test data to evaluate the model performance, but I am wondering if there is a way to tell if this training loss = 10 is a "good" one? Or does it indicate I need to use more training data to see if I can push it down to 5 or even less?
Ultimately my question is, for a well-known model with a pre-defined loss function, is there a "good" standard value for the training loss?
thanks.
you need to train your weights until avg loss become 0.0XXXXX. It is minimal requirement to detect object with matching anchor IOU.
Update:28th Nov, 2018
while training object detection model, Loss might be vary sometimes with large data set. but all you need to calculate is Mean Average Precision(MAP) which exactly gave the accuracy criteria of trained model.
./darknet detector map .data .cfg .weights
If your MAP is near to 0.1 i.e. 100%, model performing well.
Follow link to know more about MAP:
https://medium.com/#jonathan_hui/map-mean-average-precision-for-object-detection-45c121a31173
Your validation loss is a good indicator of if the training loss can further alleviate, I mean i don't have any one-shot solutions ,you will have to tweak Hyper-parameters and check on the val test and iterate.You can also get a nice idea by looking at the loss curve, was it decreasing when you stopped training or was it flat, you can get a sense of how the training has progressed and make changes accordingly.GoodLuck
I am completely new to Machine Learning algorithms and I have a quick question with respect to Classification of a dataset.
Currently there is a training data that consists of two columns Message and Identifier.
Message - Typical message extracted from Log containing timestamp and some text
Identifier - Should classify the category based on the message content.
The training data was prepared by extracting a particular category from the tool and labelling it accordingly.
Now the test data contains just the message and I am trying to obtain the Category accordingly.
Which approach is most helpful in this scenario ? Is it the Supervised or Unsupervised Learning ?
I have a trained dataset and I am trying to predict the Category for the Test Data.
Thanks in advance,
Adam
If your labels are exact then you can classify using ANN, SVM etc. But labels are not exact you have to cluster data with respect to the features you have in data. K-means or nearest neighbour can be the starting point for clustering.
It is supervised learning, and a classification problem.
However, obviously you do not have the label column (the to-be-predicted value) for your testset. Thus, you cannot calculate error measures (such as False Positive Rate, Accuracy etc) for that test set.
You could, however, split the set of labeled training data that you do have into a smaller training set and a validation set. Split it 70%/30%, perhaps. Then build a prediction model from your smaller 70% training dataset. Then tune it on your 30% validation set. When accuracy is good enough, then apply it on your testset to obtain/predict the missing values.
Which techniques / algorithms to use is a different question. You do not give enough information to answer that. And even if you did you still need to tune the model yourself.
You have labels to predict, and training data.
So by definition it is a supervised problem.
Try any classifier for text, such as NB, kNN, SVM, ANN, RF, ...
It's hard to predict which will work best on your data. You willhave to try and evaluate several.
I'm using libSVM to train a binary classifier on 38 training instances consisting of ~250 features.
Before training I scale the data to [0,1] and perform a grid search to find the best parameters. However, I get the same accuracy results for all combinations of settings. I'm wondering if this indicates a problem, and if so what problem?
Thanks in advance!