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Questions tagged [time-series]

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

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Intuitive explanation of using ACF to determine the order of MA in time series

It is intuitive to know why we can only use PACF to determine the order of AR - since ACF will show good correlations even for the lags which are far in the past, as it also cater for indirect effects ...
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ACF vs PACF in ARIMA

Given a time series problem, Should ACF and PACF be done before or after differencing that make the time series stationary? If ACF and PACF has shown different results, should the number of orders ...
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Publicly available dataset for failure prediction in servers [migrated]

Is there any open dataset available for anomaly/resource contention failure prediction in servers. It should have different timeseries features like CPU, memory usage etc. Along with a label ...
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LSTM with multiple time-series

EDIT: Now I didn't convert to list. I am training LSTM for multiple time-series in an array which has a structure: 450x801. There are 450 time series with each of 801 timesteps / time series. The ...
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How To Perform Time Series Data Patterns Classification

I'm fairly new to Python, and I want to create a Machine Learning algorithm that classifies Time Series Data Patterns. The data I will be using comes from an accelerometer-arduino system fashioned ...
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Cross-validation for Timeseries Counterfactual Analysis

We are looking to predict counterfactual states from time-series data. In our problem we are looking to determine the energy savings from a grid-installed device that is varied on and off for many ...
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CNN 1D for time series. Value Error. I did read all the possible answers in StackOverflow regardiing this problem

I been fighting with this problem for 2 weeks now. And I extensively research for solutions here and in other sites. I have a dataset of 4 dimensions device_id, time and longitude and latitude. After ...
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1answer
25 views

Measure degree of heteroscedasticity [on hold]

I analyzed my time series using Breusch Pagan test and observed the presence of heteroscedasticity in it. After box-cox transformation, I again tested the time series using Breusch Pagan test. The ...
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Categorical Multivariate Time Series

I have a small dataset of products of which the price varies along time. Each product is represented by categorical features mostly ( type, matter, use, location ...) and one or two scalar features ( ...
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Python Time Series forecast with small sample size

I was tasked with creating a Python-based time series forecast model that I could apply separately to two datasets of size n=12 and n=24. I let them know that such sample sizes would make the model ...
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29 views

Forecasting multiple time series with a single model

I have a dataset with sales numbers for ~500 different markets (assume different cities or regions) and need monthly forecasts for each market. Instead of building 500 different models, I'm interested ...
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Why might an LSTM be capable of predicting an ARMA signal but not a linear combination of ARMA signals?

I have an LSTM network and am testing it on some dummy ARMA signals. I'm trying to predict the signal 5 time steps into the future. The network is capable of outperforming Naive (persistence) when ...
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29 views

Anomaly Detection in Time Series: How to label the data

How to label time series so that we can train it on machine learning models to classify data point as anomaly or not? If I have time series, and anomaly occurs at time t, should I label that point 1 ...
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For time series forecasting task, should I use data across several time steps or singe timing data for prediction?

I have a time series forecasting project, there are over 10, 000 time steps of data, so the data amount is not a problem. At first, I thought I've to create a time-based data pipeline that forms the ...
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Sequential pattern mining on system error logs (time series, no transactions) [closed]

To preface, I am a newbie. I've done some NLP before, but I am pretty inexperienced in terms of data science. I have a set of system error logs. Each line has a timestamp, and an error message (there ...
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Similarity Measure Time Series

In my work I have an observed Time Series and Simulated ones. I want to compare the Light Curves and check for similarityto find out which simulated curve fits best respectivley which parameters ...
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Time Series Regression and Exponentially weighted mean of lag values-For Advanced Time Series Experts

I need help for a time series regression problem in engineering features. Background: The dataset has weekly data for sales/orders of hundreds of products for last 145 weeks totalling 450000 ...
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Is there a way to use Plotly as an annotation tool, for labelling time-series for instance?

I have been tasked to create a tool aimed at labelling sections and/or precise data points of a biomedical time-series. Our main framework is written in Python. I would like to know whether it is ...
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1answer
37 views

LSTM with input of actual time step

I'm working on an implementation of LSTM neural network to forecast energy consumption. I have a dataset with load, series of weather parameters and indicator of it's bank holiday or not. I first ...
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Feature selection for circular data in time-series

I'm predicting ozone concentration based on meteorological and seasonal variables. In the feature engineering stage I converted the MONTH, DAY_OF_WEEK, DAY_OF_YEAR to its sin and cosine components ...
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Multivariate time series prediction with binary target

I have an electronic component whose sensors record temperature, current and voltage values of various sub-elements. These readings are taken at regular intervals of time and I organized them as ...
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1answer
83 views

How to deploy a LSTM Model

I have trained and validated my LSTM and I would like to deploy it. So, I know that we can save and load the Sequential object of Keras (I am working with Keras as you can guess). I thus implemented a ...
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Comparing two time series data to find deviations between them [closed]

This is a use case that I have and I am trying to automate this. Any pointers would be helpful. Use Case: When we deploy any new version of a web service, we keep monitoring it (while deploying to ...
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1answer
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Understanding the multidimensional-nature of the data being fed to a RNN and its output

Assuming we have a time-series dataset whose window_size = 30 and the batch_size = 4, which makes the overall input = 4*30 (2D). But as RNN expects 3D input, ...
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1answer
24 views

Feature selection before or after applying filter in Time-series forecasting

I'm predicting ozone concentration based on meteorological variables and ozone value of the previous day. I applied savitzky golay filter to get rid of noise in the time-series dataset. My question ...
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Real-time Time-Series Error Correction

I have some sensors, each of which generates data points at mostly-regular intervals. So for each sensor I have a time series. I have to correct measurement errors from the sensors, replacing them ...
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Align data of different frequencies

My project's goals is modeling a physical system. Where I measure physical entities using sensors and try predicting future values. A problem I am facing, is how to align different sensors data in one ...
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Is it meaningful to use word2vec for non-string inputs like time series analysis?

I am working on a project that detects anomalies in a time series. I wonder if I can use word2vec for anomaly detection for non-string inputs like exchange rates?
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Which method to use to remove trend from time series?

From what I understand, differencing is necessary to remove the trend and seasonality of a time series. So I assumed it basically does the same thing as signal.detrend from the scipy library. But I ...
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LSTM Captures Trend but Regresses to 0

I am using a vanilla LSTM to predict time series data. My simple model uses an 8 unit LSTM with dropout and a time distributed layer. The model can learn the shape of the data (i.e. when peaks and ...
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1answer
28 views

Any issue with “overlapping” sliding windows in time-series data analysis?

I am developing some classification/regression models form accelerometry time-series data. So far, I have created datapoints by extracting features from non-overlapping sliding windows of the time-...
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2answers
30 views

Probability vs recall for time series classification task

I am working on the time series classification task that focuses on predicting a fault. I framed the problem as a multi-step forecasting problem, where my goal is to predict to the class at ...
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1answer
36 views

How to make forecast in LSTM with specific/defined feature values

I am analyzing the avocado dataset to predict the future prices of avocado depending on the region and type (organic/conventional). I've trained my model which seems to be working. The test results ...
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DA-RNN Keras implementation

Is there DA-RNN implementation with Keras or TensorFlow? If its a commented notebook it would be amazing https://arxiv.org/abs/1704.02971 here is the paper I am referring, I only found Torch ...
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29 views

LSTM model for many time series

Let's say one has many time series for which one wants to build a predictive model (based on LSTM). Which of the following cases would be more optimal and why? 1) building one model for all the time ...
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1answer
23 views

Hyperparameter Tuning Time Series in Production

I have a time series data that handled using GDBT to predict the next value. I always use previous 30 days data to train daily, but overtime the data to predict and train is increased because the ...
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can macd be calculated for values other than 12 and 26?

I am working on time-series classification problem using CNN. The dataset used is financial stock market data(like yahoo finance). I am using some technical indicators calculated using raw values high,...
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How to count the number of times a sequence in a panda (time) series is less than a specific duration

Given a panda series where a value is either {0, +1, -1} and the sequence always starts with a +1 and end with a -1. For example, ...
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2answers
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Trying to understand encoder-decoder sequential models in Keras?

My understanding is that for some types of seq2seq models, you train an encoder and a decoder, and then you set aside the encoder and use only the decoder for the prediction step. For example this ...
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How to time series forecast with multiple time series data sets on the same time series index

How does time series work with multiple time series data sets on the same index? For example, suppose I were a utilities company. Suppose I have the electricity usage of two homes, each indexed for ...
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LSTM validation loss not improving

I'm a noob in the ML world and am currently building an LSTM to forecast the next page a user is going to visit on a website. My dataset is pretty much a mapping (with sliding window) from one page to ...
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How to get rid off differences between prediction and actuals while predicting or forecasting time series data

I have performed Boxcox transformation on my time series data and processed it through ARIMA modeling. Converted prediction values to the actual. I see significant differences between actual and ...
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How do I organize a multi-site multivariate time-series dataset for a Random Forest Regression?

I am trying to do a Random Forest Regression to forecast the next months value. I have a few years of data split by month. In each month I have about 1500 unique sites. There are 14 features.
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How to provide future exogeneous data for lstm when predicting multiple steps

let say I have one timeseries, for which I want to predict $K$ future steps. Additionaly I have exogeneous information, for example dates of holidays, that have impact on my timeseries. Therefore my ...
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Do we need to tune same model differently for different window sizes in time series data classification?

I am currently working on the time series data classification problem using deep learning. As we all know that in time series, we process the time-series data sequentially for some time steps at a ...
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1answer
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Machine Learning based Multivariate Time Series Prediction - How to create supervised data format

Q1: I have a multivariate time series dataset. For each timestep, there are 11 features and 1 output. I am going to use supervised ML to predect the output. I understand that in univariate cases, if ...
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How to predict the class by training partial sequence as Input using LSTM's in Keras?

In parallel to this question which I have asked before, I have implemented my model based on the answers which I got from here answered by @Kbrose In this question I would like to clarify some ...
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Is it possible to use LSTM for time series forecasting for future months with test data having only NaN values?

my dataset is a univariate time series with one column as months, other column having demands for the corresponding months. My test dataset has NaN vals only for all the months. Can LSTM be used in ...
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Can my training and testing data be of different timesteps length?

I am trying to use a deep neural network for a regression problem. I have 50 samples of 600 timesteps each. I am planning to use 40 for training and 10 for validation. If it matters, I have used a ...
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Extraction of patterns using interrupted time-series analysis

I am developing a movie recommender system on an online platform. In order to evaluate influence of recommendations, I would like to extract patterns such as “recommending increase young users’ ...

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