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Questions tagged [time-series]

Time series are data observed over time (either in continuous time or at discrete time periods).

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Wavenet joint probability

As presented in the first article of Google Wavenet (https://arxiv.org/pdf/1609.03499.pdf) the model can approximate the joint probability of the whole sequence (raw audio waveform) using the chain ...
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1answer
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Multivariate Time Series Binary Classification

I have continuous (time series) data. This data is multivariate. Each feature can be represented as time series (they are all calculated on daily basis). Here is an example. ...
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Why is performance worse when my time-series data is not shuffled prior to a train/test split vs. when it is shuffled prior to the split?

We are running RandomForest model on a time-series data. The model is run in real time and is refit every time a new row is added. Since it is a timeseries data, we set shuffle to false while ...
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Train LSTM model with multiple time series

I am predicting energy usage for a bedroom within a school residential building with date, temperature and humidity as input features, using 7 time-steps and predicting for one-day (one-timestep). I ...
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How to create a timeline graph ( Date / time series) in MATLAB?

The data file is attached https://ufile.io/ttwi8.How may I create a timeline graph where my y-axis is the number of cases(Demand and prediction as sjown in attached graph) and my x axis is increasing ...
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LSTM Multi-state forecast

I follow several tutorials about LSTM multi-step forecast to solve my problem. My problem: I have a time series of price about 3 months (Jan 2018 to Mar 2018) and I assume there are seasonal. So, my ...
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De-noising/removing measurement error from time series with very few observations

So, I have a series of very small independent time series (4 or 8 observations), each measured with some potential error, the direction of which is indeterminate a priori. I need to smoothen this ...
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How to implement Moving window with LSTM for Time Series Prediction?

I am trying to implement a moving window in my dataset. The window size=14 (for instance).After implemntinf sliding window how to prepare inputs and outputs for netwok? ...
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DONUT- Anomaly detection Algorithm ignores the relationship between sliding windows?

I'm trying to understand the paper : https://netman.aiops.org/wp-content/uploads/2018/05/PID5338621.pdf about Robust and Rapid Clustering of KPIs for Large-Scale Anomaly Detection. Clustering is done ...
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1answer
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Clustering time series based on monotonic similarity

Context I am involved in a task of clustering 1500 time series of 500 observations into a few number of clusters. The time series share all the same observed property at different spatial locations, ...
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Question about designing a multi input/output LSTM with different Tx, Ty [on hold]

I like to know if my Tx is 20 but I need a Ty of 3, how should I convert this input to that output? Or specifically, I must get my 3 preferred y^s from which units? the last 3 units(a<17>, a<18>,...
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How to convert a time series data which can be fed to a Machine Leanring model for unsupervised learning?

I am very new to time series data. I am working with server data and want to classify if the bunch of server times are healthy or not. My data looks like this in pandas ...
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preparing time series data for building a rnn

I am preparing time series data for to build an RNN model (LSTM). The data is collected from sensors installed in a mechanical plant. Consider I have data for input and output temperature of a ...
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Is there an R tutorial of using LSTM for multivariate time series forecasting?

There is a great blog post about how to use keras stateful LSTM in R to forecast sunspots. I applied it to financial ts data ...
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6 views

Long run time for grid search SARIMA

I am running a grid search for identifying the right set of params for Seasonal ARIMA, for over a 1300 training set and range for all the params being 0,1 and 2. But this process is taking over ...
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Imputing ecological time series data based on known other data

I am working on a research project where I have missing daily data in one variable, Sea Surface Temperature (SST), from 1968-1981. I understand this is a lot of data to impute, but I can't afford loss ...
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1answer
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Dealing with time series data which is not continuous

I have a time series data in Python 3 as follows: ...
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Quantify similarity between 2 multivariate time series samples

I am looking for methods to analyze the similarity between 2 multivariate samples of a time series data. I have tried and analyzed KS test, euclidean distance and correlation based methods. What are ...
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How can i find trend time for my articles?

our article is time-based, that means is my article search more in a specific time. as you can see in under chart this article search more in specific period time. if my dataset looks like this(it ...
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KShape cluster centers offset?

I am using tslearn KShape to cluster time series data. I am generally happy with the results, as upon inspection, the clusters seem to make sense because of the similarity in shape and magnitude. I ...
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Multi-Step Forecast for Multivariate Time Series (LSTM) Keras

I have been trying to understand how to build LSTM model for multivariate time series forecast using Keras but I am still unsure how to present the data in the correct shape. ...
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Initialising states in a multilayer sequence to sequence model

With a sequence to sequence model where the enocoder and decoder are both comprised of one layer each, the initial state of the decoder is initialised to use the final states of the encoder layer. In ...
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Extend forcasting steps greater than 100? in time seires [closed]

So we are attempting to forecast the prices of stocks from yahoo finance for our senior project. We have a projection up in a line graph but it will only forecast up to 100 data points (days I ...
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1answer
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How to find correlation between time-series of different units?

I have 3 time-series data. NDVI(normalized difference vegetation index) mean Precipitation Temperature All of these have their own unit. Now I want to find similarity/correlation between NDVI and ...
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1answer
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How do I use LSTM Networks for time-series classification problems?

I have 2 binary outputs (1 and 0) with time series data. The dataset order is shown in the image..Can anyone suggest me how to handle this problem with LSTM? Particularly in MATLAB or Python. Thanks ...
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Neural Network Architecture for batch of time series data

Let's say I have a data set which is a 2-Dimensional Matrix as the input and I want to predict either 0 or 1 with regard to the entire 2-D matrix. Now each row in the 2-D matrix is a time series, i.e....
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Multiple season decomposition with Seasonal-Trend Decomposition (STD)

Seasonal-Trend Decompositions allows to decompose a time series into the components of Trend, Season and Residual (noise). See this article I would like to use the same approach, but decomposing a ...
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Sliding cross-correlation [closed]

I want to understand Timeseries shape similarity algorithm ( Shape-based distance aka SBD). I can't understand the statistics behind it and why it is better than DTW or other similarity measure. I'm ...
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1answer
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What is the best way to predict time series data? [closed]

I have monthly price data for tomatoes for the last 9 yrs for a particular town and I'm looking to predict the prices of tomatoes 6 months into the future. I had considered using Linear Regression in ...
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what is the interest of TimeDistributed after an LSTM layer?

I've already seen several similair questions but I did not understand anything, what is the interest of TimeDistributed? why we need to insert a TimeDistributed layer after LSTM to establish the time ...
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Time Series prediction for uneven data with some data provided

I am facing an issue with a time series prediction problem. My data looks like the following: Datetime , Feature 1, Feature 2, Feature 3, Feature 4 19-03-2015 02:15 ,80 ,50 ,16 ...
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What should the size of the decoder output be in a sequence to sequence model

In a sequence to sequence model, a lot of the tutorials I have read state that the decoder target length should be the same as the encoder input length (https://blog.keras.io/building-autoencoders-in-...
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1answer
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ML technique to predict next year output based on text quantities [closed]

I have a random data that I would like to predict how much a quantity will be in 2020. The data looks like this: ...
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Connectionist Temporal Classification Loss for Astroturfing Detection

I'm trying to detect astroturfing in social networks through post timestamp patterns. That is, if it's the same person posting across several different accounts, then these accounts are expected to ...
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How to calculate customer purchase interval and predict next purchase in python?

Suppose we have a data set consists of columns TransactionId, CardNo, TransactionDate then how can we calculate the customer purchase interval (means if customer A purchased on Jan 1st and after ...
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How to methodologically show that a given 'time-series/sequential' data is not really sequential?

I have an apparent time-series/sequencial (supervised: multi-class classification problem) dataset with each data-point time-stamped. However, some domain intuition tells me that the data is static ...
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Bugs in Pytorch replication of a simple LSTM model built with Keras

I am new to Pytorch. I am trying to replicate a simple Keras LSTM model in Pytorch. Two model takes in the exact same data but the Pytorch implementation produces a significantly worse result. In my ...
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How to solve a classification problem when the independent variables/covariates/feature vectors form a time series?

Cross posted here: https://stats.stackexchange.com/questions/389189/how-to-solve-a-classification-problem-when-the-independent-variables-covariates, but no answer; hence trying here as well! I hope ...
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Is there a disadvantage to letting a model train for a large number of epochs?

I created a model to solve a time series forecasting problem. I had a limited amount of time series with which I could train the model therefore I decided to augment the data. The data augmentation ...
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Any thoughts on how to fill missing (isolated, and ranges) annual data to improve accuracy for future predictions

The purpose is to have a better training set, it's a multivariate timeseries ranging from 11(November) to 8(August). So 9(September) and 10(October) are totally missing. Here is an extract, a one ...
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Multivariate time series forecasting without historical data

I have a dataset similar to the example below: It contains some categorical data (e.g. business type, business size, location) and the time-series of energy consumption from Time 1 to Time N with a ...
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Tuning a sequence to sequence model

I have written a variable length sequence to seqeunce autoencoder in keras using this tutorial as a guideline: https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras....
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How to handle non consistent time series( using LSTM )

The time series dataset I am working on has missing samples. I am trying to use keras and LSTM for prediction. How should I handle the missing timestamp samples ( sometimes there are missing weeks ...
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Comparison between approaches for timeseries anomaly detection

After various days of research, I could take a global picture of the existing methods to perform anomaly detection on time series, namely: Forecasting with Deep Learning. Eg. RADM or LSTM model ...
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Which methods exist to find correlations between multiple univariate timeseries anomaly detection output?

In this short article from Anodot, they explain the (dis)advantages of directly applying a multivariate anomaly detection model on raw timeseries data. Instead, they look for anomalies on each ...
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About applying time series forecasting to problems better suited for reinforcement learning, like toy example “Jack's car rental”

"Jack's car rental" is an example of a reinforcement learning problem, proposed in the Sutton & Barto book, in which the goal is to optimize the daily distribution of cars in two locations of the ...
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Forecasting vs non-forecasting predition for time series anomaly detection

I have got the objective of implementing a uni/multivariate online anomaly detection system. After multiple days of research, I could collect many ways to achieve this (Eg. moving average solutions ...
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Monthly trend with fb prophet

I have monthly data with month/year in one column and price on another. I would like to get a yearly trend with fb prophet library in python (how to use monthly data with the library is explained at ...
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1answer
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Test for heteroscedasticity in time series

I want to test heteroscedasticity in time series. The tools in python like: statsmodels.stats.diagnostic.het_breuschpagan require residuals as input obtained by fitting model to data. Since this kind ...
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PCA on conditional heteroscedastic timeseries

What is the correct method of application of PCA on time series data. Since the time series may exhibit conditional heteroscedasticity, application of normal PCA might be wrong as the variance changes ...