Questions tagged [arima]
arima (autoregressive integrated moving average.) It's a model used in data science to measure events that happen over a period of time. The model is used to understand past data or predict future data in time series.
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Seasonal ARIMA?
Greatly enjoy exploring data in Orange Data Mining!
I have daily average temperature data for several years. I can plot the periodogram, and do a seasonal decompose.
Is there a way to forecast the ...
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Covariance of forecasts from Python/statsmodels SARIMAXResults object
I have a SARIMAX model fitted at daily frequency using statsmodels.tsa.statepsace.sarimax. It is a "full" SARIMAX model in the sense that it has AR, MA, ...
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Time series forecasting: Youtube Views
I have some monthly data for 200~ videos from a youtube channel. I can see how many views each video got each month. Videos were released consistently each week and there is no missing data.
Usually a ...
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Is it normal for a SARIMA model to produce no residuals?
I'm working on my first time series project where I am required to produce predictions for financial data.
The raw data is below:
Clearly, there is a seasonality and downward trend, I used the ...
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Consumption rate on a small dataset with variability
I am looking to find the consumption rate, or how fast I am consuming energy so that I can later predict when my energy will reach a certain threshold. My dataset is fairly small and looking to see ...
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Model for predicting temperature data of fridge
I set up a sensor which measures temperature data every 3 seconds. I collected the data for 3 days and have 60.000 rows in my csv export. Now I would like to forecast the next few days. When looking ...
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Why so discrepancy between ARIMA and LSTM in time series forecasting?
I have this time series below, that I divided into train, val and test:
Basically, I trained an ARIMA and an LSTM on those data, and results are completely different, in terms of prediction:
ARIMA:
...
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Reformulation of ARIMA for an ML context
Is the following approach to reformulate the ARIMA model for $\varepsilon_{t}$ correct?
I want to minimize $\sum{\varepsilon^{2}_{t}}$ but I only have a time series $y_t$ and the two are coupled such ...
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How to make ARIMA model out of sample forecasts with exog Fourier terms using weekly data? (Python)
I'm a bit confused on how to make out-of-sample predictions if I have Fourier terms included in my ARIMA model. I am using Fourier terms to model annual seasonality as per the advice given in ...
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Python (S)ARIMA models completely wrong
I have some time series, like this one:
I want to predict future values, so I splitted in train/test (70/30) and I created several ARIMA models, however they are all completely wrong (or maybe I am ...
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Which data to use to plot ACF/PACF while determining the ARIMA parameters?
I am confused about how to plot ACF/PACF. I do STL decomposition to time series data and then take the residuals and plot ACF/PACF to determine the ARIMA parameters.
Most examples, however, take the ...
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ARIMA predictions look shifted by one unit of time
I am using statsmodels ARIMA (1,2,1) to predict the monthly demand for a product. The predictions look like they are shifted to the right by one month. I wonder if the statsmodels.ARIMA.Residuals....
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How should a dataset looks like for Time series forecasting
What should a dataset look like for time series forecasting?
Can I do time series forecasting with a dataset that contains apartments from ad sites obtained with:
web scraping from 2018 to 2021
13 ...
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Which dataset for multivariate time series forecasting
I'm trying to forecast Real estate Price , it's not a prédiction. But a forecast Like the Price of a an appartement in 2023 or 2024, i'm asking about how should be my dataset ?
Can I use a dataset ...
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Spike and dip cleaning in Big Query ML ARIMA_PLUS
If there is sales of a product spiking once every year (let us say every year in May). Will the ARIMA_PLUS model of Bigquery clean every year May sales as spike ...
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Negative forecasts in ARIMA
Can ARIMA (specifically BigQueryML ARIMA_PLUS) give negative forecasts even if the training data has only 0 or positive values?
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Confidence/prediction interval vs lower and upper bounds in ARIMA_PLUS big query
How is CI intervals in ML.forecast and lower-upper bounds in ML.Detect_Anomalies different for Arima_plus model in BigqueryML ?
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Anomaly detection and root cause analysis
ARIMA is widely used for anomaly detection on time-series data e.g. stock price prediction. ARIMA assumes that future value of a variable (stock price in our case) is dependent on its previous values. ...
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Applicability of ARIMA model on non stationary data
I have a time series dataset that does not have the stationary property. The dataset is monotonically increasing or sometimes showing no change over periods of time. Can I apply the ARIMA model to ...
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How to handle multi time series data for 10K + items
There are 50 shops and each shop have 30000 items.
Goal is to forecast the sale of item based on shop.
Forecase the item_cnt_day, for this i dont see this as multi ...
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How to revert np.log(data) and data.diff()?
I have used np.log(data) and then applied data.diff() to transform my data in timeseries model. I have the predictions. How do I ...
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Does this ARIMA model take seasonality into account?
I'm writing a tutorial on traditional time series forecasting models. One key issue with ARIMA models is that they cannot model seasonal data. So, I wanted to get some seasonal data and show that the ...
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How can we make forecasts from stationary data
I'm confused about the concept of stationarity. Most definitions require the mean and Variance to be constant 'over any interval'. This statement confuses me, if any interval should have the same mean ...
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How to compare different forecasting models over different time horizon?
Developed multiple Models with AR, ARIMA, VAR; LSTM , SARIMA.
Now, the purpose is to find out which model performs best on a given use case with different time horizons.
The time series data is ...
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Grouped Time Series forecasting with scikit-hts
I am trying to forecast sales for multiple time series I took from kaggle's Store item demand forecasting challenge. It consists of a long format time series for 10 stores and 50 items resulting in ...
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What are more advanced techniques than ARIMA?
For timeseries predication cases, what other techniques are available in statistics or machine/deep learning other than MA (moving average), ARMA, and ARIMA?
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ML model to forecast time series data
This question has three sub-parts, answering each of which probably doesn't require huge text. I hope that is okay.
I'm trying to understand time series prediction using ML. I have the target variable ...
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How to know if a time series sequence is predictibale or just random (Univariate time series prediction)?
I'm trying to predict a current value of a variable based on the its previous 10 values. I tried multiple time series approaches including ARIMA, LSTM and linear regression... None of them really ...
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Best approach for univariate time series predictions?
I have a univariate time series. where I'm trying to predict a current value of a variable based on the previous 10 values of the same variable. I tried three approaches:
1- linear regression where I ...
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First Differencing to remove seasonality and trends
I am trying to remove seasonality and trends from my time series data. I found this post that said to use df_diff = df.diff().diff(12).dropna() (https://www.tobiolabode.com/blog/2020/12/30/how-to-...
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Predict the first observations of a time series when order of the model is higher
Suppose you have you have a time series with 365 observations, one for each day of the year, and you split the first 183 rows in training set and the latest 182 in test set.
Suppose you create an AR (...
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How can i find the accurcy for time series? [closed]
I have to work for the first time with time series and I have some question about this interesting field of machine learning.
What I have to do:
I should make a forecast for quantity for some special ...
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Python: SARIMAX Model Fits too slow
I have a time series data with the date and temperature records of a city. Following are my observations from the time series ...
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ARIMA training super slow
I am fitting ARIMA model (from statsmodels) on 20 000 elements dataset on a 24 CPU 200+GB RAM cloud server for over 24 hours now. It loads all the CPU's. But It takes so long... Is it how it works or ...
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ARIMA d Parameter and Explicit Differencing
Can I use only d parameter for ARIMA instead of applying differencing to data before training and applying inverse transform to forecasts in order to get them into original scale? Do libraries like ...
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AR coefficients are not stationary
I have a timeseries data and I want to forecast it by applying ARIMA. After reading data, I decomposed it to analyze its components and get an idea whether it is stationary or not. It seems there is ...
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auto_arima results summary (intercept?)
I ran auto_arima on my model from the library pmdarima and I'm trying to interpret the results printed to the console. I see two examples of similar parameters that yield different results:
ARIMA(0,1,...
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Narrow confidence Interval of forecast
I am new to data science so please accept my apology in advance. I am trying to predict the value using ARIMA. I have got weekly value for the current year. Based on the available weekly values, I ...
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Time series forecast for small data set
I am new in data science so please accept my apology in advance if my question sounds stupid. I want to do a time series forecast of outage mins in the current regulatory year. The regulatory year ...
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Best common metric for comparing classic time series forecasting methods (ARIMA/Prophet) with ML approach?
I am new to time series forecasting and looking to compare the performance of ARIMA/Prophet with an XGBoost model in predicting future stock market values based on historical stock market data and ...
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Time series forecast for everyday for till a distant future
I have time-series data for every single day from the last 5 years with seasonal variation and a general increase in trend. This is what my data looks like:
And I am trying to predict for every ...
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Ljung-box test on weekly percentage of total quarter bookings
I have a data on the weekly percentage of the total quarter bookings. The data looks as follows (note: weekly percentages add up to 100 for each quarter) :
(not real data)
I used the Ljung-box test ...
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High error Arima model - Python
I have a time series data. It has daily frequency.
I want to forecast the data for the next week or month with an ARIMA model.
This is a chart of my time series data:
First I use the method ...
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How to automate Seasonal Arima?
I am building Seasonal Arima for more than 10k products. In all the tutorials and blogs mentioned, I need to do the exploration to find the p,d,q values along with seasonality value using the ...
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Understanding lag plot ,ACF plot and auto-correlation plots
I have a data-frame of 2809 rows which is an unevenly spaced time-series
...
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ARIMA: How to understand performance of the model?
I am new to use of ARIMA model and after working on it for a couple of days and doing research - I'm not sure how to interpret the performance of my model...
Here is what the ...
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Choosing the periodicity in a SARIMA model
Given the order (P,D,Q,s) of a SARIMA model, s is an integer representing the number of periods in a season. Intuitively, I suppose it would be 12 for monthly data ...
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Why is my prediction using ARIMA better if I'm using less historic data?
I have a data set containing hourly electricity prices for since 1.01.19 until September. Since the process turned out to be (weakly) stationary, I applied an ARIMA model in Python in order to predict ...
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Forecasting via LSTM or XGBoost... is it really a forecast or
I guess I understand the idea of predictions made via LSTM or XGBoost models, but want to reach out to the community to confirm my thoughts.
This tutorial does a nice job explaining step by step of ...
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