Questions tagged [time-series]

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

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27 views

How to predict a certain time span into the future with recurrent neural networks in Keras

I have the following code for time series predictions with RNNs and I would like to know whether for the testing I predict one day in advance: ...
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1k views

Input for LSTM for financial time series directional prediction

I'm working on using an LSTM to predict the direction of the market for the next day. My question concerns the input for the LSTM. My data is a financial time series $x_1 \ldots x_t$ where each $x_i$...
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Does time-series dependence penalize prediction with CNNs?

I'm trying to use CNNs on time series data (EEG), measured on different people. Each person has 10-20 recorded signals of different lengths and every subject has one global class assigned. Example: ...
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I want to apply Time Series Clustering to time series data consisting on: Level & Growth, but I have only found algorithms for one series

Is there any implementation of Time Series Clustering which allows me to segment using two or more series of the same phenomenon both as input for the algorithm? Suppose I have $A_{i,t}=X_{i,t}$ and $...
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2answers
284 views

how to calculate p value

I need to calculate statistical significance between two time series, each with 4500 terms. My null hypothesis is that $H_0: \mu=\mu_0$ (value in time t of first time series is equal to value in time ...
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Forecasting Multiple (few hundreds) uni-variate time series with inflated zeros

I am a novice seeking help to gain experience in Data Science. Let us take a scenario where a big company would like to forecast its sales (a specific product) across different stores in different ...
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1answer
1k views

Bayesian optimization with Keras tuner for time series

Goal: trying to use walk-forward validation strategy with keras tuner for time series when training a neural network (mainly LSTM and/or CNN). Did anyone find a direct way of doing this? One ...
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1answer
676 views

Time Series pattern recognition and classification problem

I have some labeled sensor data. Now, I would like to know how to extract features from time series using DFT, DWT, and HAAR transforms. I know that the transformations above transform a signal to ...
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1answer
218 views

Machine learning for missing data in time series

We have two time series columns - column A is the reference column ( source of truth) and column B is a ''cousin'' of column A, in the sense that it exhibits ( or should exhibit) the same patterns, ...
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1answer
39 views

How do I minimizie cost for EV charging?

I want to find a charging schedule that minimize cost of charging an EV. The main objective is to have a fully charged car for the next morning, but the sub objective is to minimize cost based these ...
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1answer
252 views

Target Variable Encoding for Time Series Change point detection

I am working on a time series data for which I intend to impliment machine learning model for detecting change point in time series data. This data is recorded fom machinary and we have to predict ...
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0answers
26 views

How do I use number of hours as index in timeseries forecasting?

I have a dataset that has number of hours (consecutive value) and total sales in that 1 hour in my dataset. See below for head of the dataset: ...
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13 views

How does state information get transferred when predicting with LSTM

I have a typical mutivariate time series forecasting problem that I want to solve using an LSTM, with mutliple features in the input sequnce and one feature in the output sequence. If I train my LSTM ...
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1answer
156 views

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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1answer
100 views

LSTM model prediction is almost constant

I am new to RNN and LSTM and currently experimenting with different settings. When trying to model time series data in absolute terms (predicted close price), I am faced with the following problems: ...
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1answer
34 views

How to approach mapping families of vectors on a lattice and forecast resulting value

I describe here a model to describe how neighbours influence a node. I wish to implement it to attempt forecasting to values associate nodes; I post here asking for suggestions on mathematical model ...
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2answers
115 views

Similarity between two time series with different sampling frequency, different amplitude, and different lengths but taken from the same source?

I have two files with accelerator readings and I want to get some metric/ measurement to get the similarity between these two files. I have tried Pearson’s R coefficient, dtw distance, dtw score. ...
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1answer
376 views

How to do time series regression without scikit and numpy in Python?

On a recent Hackerrank interview I was faced with the following problem: Given a set of timestamps (format 2019-11-26 11:00) and their corresponding stock prices (single float value), approximate ...
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1answer
60 views

How can you build a model based on non-independent imbalance data?

I am trying to predict customer churn based on the data that I have. I am defining churn as an activity that is not followed by another activity within a week. The customer might come back in two ...
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1answer
343 views

How to approach Peak picking with a wide range of peak shapes, sizes, varying noise level, and occasionally shifting baseline?

I am trying write a program that continuously tracks the location a peak. To do that I need a very good peak detection algorithm. It not only has to tell the location of the peak but also the absence ...
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3answers
263 views

How can one generate future forecasts from probabilistic events?

I have an event "whether an item sold will be returned or not" which I can predict with a certain probability based on information gathered at the time that the purchase occurs (product features, ...
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1answer
159 views

Identify "steady-state" time series window

I'm new with the time-series analysis. I have several time-series (noisy of course) part of the same set of measurements (sampled simultaneously). The time series are the results of a stochastic ...
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1answer
157 views

Sliding window approach using SVR & LightGBM

I'm working on a multivariate time series forecast using a couple of ML algorithms (Neural Networks, Support Vector Machines & Gradient boosting algorithms). I need to measure the performance of ...
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1answer
39 views

Google's Bayesian Structural Time-Series

I am attempting to get my head around Google's Causal Impact paper, which isn't completely clear to me. In the methodology part of the paper, the authors say: "The framework of our model allows ...
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1answer
114 views

What are the key differences between a MLP with lagged features and a RNN

I've been working with MLP's for a while. Whenever I assumed that the past values of a feature might be useful for predicting the future values of Y, I would just create a new column in my data frame ...
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1answer
219 views

Algorithms for Anomaly Detection of Event Sequence Data [Python/R]

I am building an anomaly detection system of event sequence data (transactions). For each timestep, a transaction can be in any of 76 different stages. My dataset is therefore a 3D array of size(m,t,N)...
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1answer
24 views

How many features should be there in a dataset to apply any feature selection method?

I am working on a time series, regression problem, where I have 10 features and 180 observations. I would like to understand what the minimum number of features should be in a dataset to use feature ...
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1answer
225 views

Time series data combined with multiple feature. Predicting difference from the mean

I'm trying to predict the % attendance of people to gym classes that have previously been booked. It is heavily dependent on the time of day and also a load of other features (is it raining, fraction ...
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1answer
66 views

Facebook Prophet add Regressor

I've been searching for a long time to answer my question, but I haven't found anything. So I hope you can help. I'm searching for an opportunity to add a regressor to my prophet model in python. I ...
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0answers
17 views

oversampling multivariate time series data

For some classification needs. I have multivariate time series data composed from 4 stelite images in form of (145521 pixels, 4 dates, 2 bands) I made a classification with tempCNN to classify the ...
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1answer
61 views

How an input data flows through an lstm layer cells?

I make this sec2sec NN model for the purposes of learning: ...
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3answers
15k views

How to evaluate performance of a time series model?

I trained a LSTM network on a time series dataset. Predictions seem to follow the dataset. In fact, they are nearly a right shifted form of real values. Thus, in my opinion, it doesn't provide any ...
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0answers
18 views

Methods to combine datasets from different time periods

Consider a multivariate time series forecasting task where I have two datasets A and B. A goes from 1960 to 2020 and B goes from 2010 to 2020. There is a feature f ...
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1answer
50 views

How to split pieces of dataframe and create new dataframes based on it?

I have the following dataframe that I need to split bu Product and create a new dataframe for each product contain the series ds and y. How can I do that?
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1answer
56 views

Is it possible to update data and retrain just one of several data series in bigquery model

I am building something very similar to this BigQuery ML example project. My system is different in two ways: Firstly it will need several thousand time-series so I would prefer to use the multiple-...
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1answer
35 views

Should I concat multiple stock timeseries datasets into one?

I have several timeseries datasets of stock data, with fundamental indicators. I would like to build a model that selects stocks for buy and hold. I understand that to perform this task I have two ...
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1answer
48 views

Do Recurrent Neural Networks assume stationarity or just a general kind of sequential dependence?

Just when I thought I had convinced myself that RNNs make no other assumption about a sequence other than that there are dependencies between the inputs and that (in the case of monodirectional RNNs) ...
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0answers
20 views

Dividing a data set into segments with consistent inner behavior, using segmentation algorithms and metrics for consistency

Context of the problem: I have signal data which was recorded in a software system and which shows the runtime of multiple processes over time. In total there are more than 900 processes each having ...
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2answers
1k views

Triangle Pattern Recognition on Financial Market with Python

I'm working on a personal project to find Triangles on any stock in Python. I detect the max and min points (shift(-5,+5) because if I consider only shift(-1+1) I have a lot of lines) and write lines ...
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1answer
76 views

What is the preferred approach for this problem?

I have the Data of 10,000 users Time Session in a website/App, The Login time, logout time, the person activity, The Data is available for 60 days ( per user ) Using this 60 days data for 10k ...
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1answer
34 views

which statistical parameters are more useful to detect anomalies and outlier? mean max min var?

This time series contains some time frame which each of them are 8K (frequencies)*151 (time samples) in 0.5 sec [overall 1.2288 millions samples per half a second) I need to find anomalous based on ...
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12 views

Does this make data leakage in time series? # need help for understanding time series data

Does this make data leakage in time series? I already read this, data leakage when scaling time series Data leakage is when information from outside the training dataset is used to create the model. ...
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1answer
166 views

Time Series Classification with multiple rows per date

I have a time series data set with the lifecycle of 9000 different B2B sales leads. What I call lifecycle consists of a dataset with one registry per day for every different sales Lead identifier with ...
2
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1answer
112 views

What should I do with the NaN values on this stock quote data?

I concatenated 3 stock quote data-frames all with date-time indexes. However, they differ in starting dates so the resulting data-frame contains NaN values for the stock quotes with more recent ...
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2answers
88 views

Machine Learning algorithm for detecting anomalies in large sets of events

Let's start with the following hypothetical preconditions: There is traffic: normal and anomaly. Each traffic sample contains a list of events (of variable size) Events happen in order, the possible ...
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3answers
57 views

Logistics Demand Forecasting with 20k Different Time Series

I'm trying to tackle a very challenging problem and I would appreciate your help. My organization has a lot of different items which can be demanded by our clients. Those items can also be returned ...
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1answer
210 views

Algorithmically extract seasonality in time series data

Suppose you are trying to measure if the seasonality of a particular event stream is consistent i.e. the events in a time series happen more or less in pattern like fashion. How can you ...
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1answer
237 views

Problems to understand how to create the input data for time series forecasting with a recurrent neural network in Keras

I just started to use recurrent neural networks (RNN) with Keras for time-series forecasting and I found this tutorial Forecasting with RNN. I have difficulties understanding how to build the training ...
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1answer
12 views

Serial time conversion in python

I have an array of data "SerTime" which is the sequential time in days, from the start of the series(2016-2018). I am not sure if this is serial time or not. I want to aggregate the data ...
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0answers
9 views

Synchronizing timestamps between multiple sources of time-series data [closed]

I have time-series data coming from multiple sensors. The data from each sensor arrives at a "roughly known" interval (i.e. once every 6 hours for example), but the timestamps across the ...

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