Im trying to make some predictions for temperature data in InfluxDB and visualizing it with Grafana. I already tried to make some predictions in python with holt winters for this data and was successfull. The seasonality repeats every hour plus minus some minutes so when my data has a frequency of 10 minutes my seasonal component should be 6 or 7. When I tried those parameters for holt winters in pyhton everything works fine. But now I tried to implement it in influxdb so I can also see predictions for live data and it does not work at all.
Query B is holt winters. I group the data by 10 minutes which is why the seasonal component should be 7 or 6. You can even count the datapoints between the peaks and its always 6 oder 7. So why is the prediction so far off? Am I getting something wrong?
Related
GOAL
Every 30 mins I get a new bunch of price related data:
CurrentDatetime, CurrentPrice, Feature1, Feature2
I want to predict the price in 2 hours from now, so 4x30mins (4 steps into the future)
PROBLEM DESCRIPTION
I am puzzled what google vertex auto-ml forecasting is doing and if I can trust the results I am getting. Also unsure how to use a trained model for batch predicting.
WHAT I DID
I think the way to set up the training dataset is to add:
TargetDateTime column (2 hours ahead of CurrentDatetime)
TargetActualPrice column (the actual price 2 hours into the future)
TimeSeriesId column (always equal to 1 as all the data is one time
series).
This means, every 30 mins I now have:
CurrentDatetime, CurrentPrice, Feature1, Feature2, TargetDateTime, TargetActualPrice, TimeSeriesId
I use this dataset to train an auto-ml forecast model, setting:
"series Identifier Column" to TimeSeriesId,
"Target Column" to TargetActualPrice,
"Timestamp Column" to TargetDateTime
"Data granularity" to 30mins
"Forecast Horizon" to 4
"Context Window" to 4 (use last 2 hours of historic data to predict next 2 hours)
Split train/val/test chronologically (on TargetDateTime as is Timestamp column)
This model trains and gives some results.
Looking at the saved test data set, I can see 4 rows for each TargetDateTime, with a predictedvalue column containing a price prediction and a predicted_on_TargetDateTime column which goes from CurrentDateTime to TargetDateTime in 30 mins intervals.
This makes sense, for every 30 mins of input data, the model makes 4 predictions, each 30 mins into the future, ending up with a prediction 2 hours into the future. Great.
PROBLEM 1 : Batch predictions
I get confused when I try to use this trained model to make batch predictions. The crux of the problem is that Vertex will look at the batch input dataset, find the first row (30 min input data) for which there is no actual price data yet (TargetActualPrice is null) and then predict the next 4 steps (2 hours). This seems to mean, to make a next prediction, I would need to wait for the actuals of the previous prediction. But that means, when I get the next set of input data (30 mins later, and 1.30 hrs out from previous prediction target), I cannot use the model to make a new prediction cause the previous prediction has not TargetActualPrice yet.
To make it more explicit, suppose I have the following batch data:
CurrentDatetime
CurrentPrice
Feature1
Feature2
TargetDateTime
TargetActualPrice
TimeSeriesId
11:00
$2.1
3.4
abc
13:00
$2.4
1
11:30
$2.2
3.3
abd
13:30
$2.5
1
12:00
$2.3
3.1
abe
14:00
$2.6
1
12:30
$2.3
3.0
abe
14:30
$2.7
1
13:00
$2.4
2.9
abf
15:00
null
1
13:30
$2.5
2.8
abg
15:30
null
1
14:00
$2.6
2.7
abh
16:00
null
1
14:30
$2.7
2.6
abi
16:30
null
1
In the batch data above, I have 2 hours (4 rows) of historic data with actuals (11:00-12:30). Current time is 14:30 so I don't have actuals for 15:00 yet. The last prediction made was with the 13:00 input data (as it is the first row with actual data = null). The 13:30 - 14:30 rows I cannot use for a new prediction until I have the 15:00 actuals.
This doesn't make sense to me. I should be able to make a new 4 hour prediction every 30 mins? I must be doing something wrong?
Is the solution that, when I get the next 30 mins of input data, should I put the last predicted value into the actuals column (and update with real actuals once I have it) to proceed with next prediction? Seems cumbersome.
PROBLEM 2 : Leakage
My other concern with this is how Vertex is training and calculating the results. I am worried that when (during training) Vertex picks up the next row of 30 mins data, it will create a prediction based on the previous 4x30 mins of data (2 hour "Context window") INCLUDING the TargetActualPrice data for those rows. But this would be incorrect, as the TargetActualPrice value is 2 hours into the future and not yet available when the next 30 mins of data comes in. This would mean leakage of actual data, predicting using actuals before they are known (ie cheating).
SUMMARY
In summary, I am hoping someone can tell if I am setting the dataset up incorrectly, and/or how to batch predict every 30 mins.
With regards to my leakage concern, it originally came about because I didn't understand the batch predictions, and it seemed that every 30 mins I needed the price from 2 hours in the future in order to update the actuals of the previous prediction and then create a new prediction, which is obviously unknown at that moment.
Now my understanding is that during training, even though every 30 min timestep will have an actual price column for 4 hours in the future, the model will only use actual prices available at the moment of prediction. If making a prediction at 14.30, the model will use 14.30 data (and historic data before 14.30), and then make 4x30 mins predictions. These four predictions do not use nor are influenced by the 30 mins data after 14.30 (15.00, 15.30, 16.00, 16.30). Nor does it use the 16.30 actual price value which is the target column on 14.30 row of data.
I think I now understand the batch predictions.
Every 30 mins I get a new set of data (Feature1 and Feature2) as well the CurrentPrice. I just add this data to the batch table with TargetActualPrice set to NULL and TargetDateTime set to 2 hours in the future. I also update the TargetActualPrice in the previous row(s) in the batch table (with TargetDateTime equal to current time). Now I can run the model against this batch table and get a prediction for a max of 4 rows with TargetActualPrice = NULL.
For clarity, in the batch table, I end up with 4 TargetActualPrice = NULL rows, matching the prediction horizon of the model. When
I run the prediction, I will get 4 prediction values for these NULL rows.
I'm using TFT (Temporal Fusion Transformer) from Pytorch-forecating for the first time for my forecasting project. Im quite confused by a few things:
forecasted time series is very flat, what are the possible reasons for this? is it my train data set is too short? the train data set length is between 1 max_encoder_length and 2 times of max_encoder_length, ratio of max_encoder_length:max_prediction_length is 4:1;
how does optimize_hyperparameters work? will it update the model with best parameters after being run? or I should manually update the model with output values of the process? Im asking because after I run it, nothing seem happened, the model remain unchanged.
I want to process the value from InfluxDB on Grafana.
The final demand is to show how many miles the current vehicle has traveled in a certain time frame.
You can use the formula: average velocity * time.
Do the seniors have any good methods?
So what I'm thinking is: I've got the mean function for the average speed over a fixed period of time and the corresponding mileage, and then I want to add all the mileage together. How do I do that?
What if you only use SQL?
1.) InfluxDB uses InfluxQL, not a SQL
2.) Your approach average velocity * time is innacurate
3.) Use suitable InfluxDB functions, I would say INTEGRAL() is the best function for this case + some basic arithmetic. Don't expect the 100% accuracy. Accuracy depends heavily on the metric sampling, e.g. 1 minute sampling - but what if vehicle is driving 59 seconds and it is not moving for that second when sampling is happening. So don't be supprised, when even 10 sec sampling will be inacurrate.
I have a discrete time series covering 49 quarters between January 2007 and March 2019, which I am trying to analyse. Before undertaking various forms of analysis I wanted to check for the existence of seasonality and have tried to methods for such in R. In the first I used the WO function (Webel and Ollech) from the seastests package, which informed me that the data did not display seasonality.
library(seastests)
summary(wo(tt))
> summary(wo(tt))
Test used: WO
Test statistic: 0
P-value: 0.8174965 0.5785041 0.2495668
The WO - test does not identify seasonality
However, I wanted to check such again and used the decompose function, from which I got the below, which would appear to suggest a seasonal component. Can anyone advise if;
I am reading the decomposed data correctly?
AND
Why there is such disagreement between decompose and the seastest results?
The decompose function is a simple function that basically estimates the (moving) period average. The volatility of your time series increases strongly in the last years. Thus the averages may pick up on some random increases. Also, the seasonal component that you obtain using the decompose() function will basically always look seasonal.
set.seed(1234)
x <- ts(rnorm(80), frequency=4)
seastests::wo(x)
plot(decompose(x))
Therefore, seasonality tests are preferable to assessing whether a time series really is seasonal.
Still, if you have information that the data generating process has changed, you may want to use the test on the last few years of observations.
Lets start off with "I know ML cannot predict stock markets better than monkeys."
But I just want to go through with it.
My question is a theretical one.
Say I have date, open, high, low, close as columns. So I guess I have 4 features, open, high, low, close.
'my_close' is going to be my label(answer) and I will use the 'close' 7 days from current row. Basically i shift the 'close' column up 7 rows and make it a new column called 'my_close'.
LSTMs work on sequences. So say the sequence I set is 20 days.
hence my shape will be (1000days of data, 20 day as a sequence, 3 features).
The problem that is bothering me is should these 20 days or rows of data, have the exact same label? or can they have individual labels ?
Or have i misunderstood the whole theory?
Thanks guys.
In your case, You want to predict the current day's stock price using previous 7 days stock values. The way your building your inputs and outputs require some modification before feeding into the model.
Your making mistake in understanding timesteps(in your sequences).
Timesteps(sequences) in layman terms is the total number of inputs we will consider while predicting the output. In your case, it will be 7(not 20) as we will be using previous 7 days data to predict the current day's output.
Your Input should be previous 7 days of info
[F11,F12,F13],[F21,F22,F23],........,[F71,F72,F73]
Fij in this, F represents the feature, i represents timestep and j represents feature number.
and the output will be the stock price of the 8th day.
Here your model will analyze previous 7 days inputs and predict the output.
So to answer your question You will have a common label for previous 7 days input.
I strongly recommend you to study a bit more on LSTM's.