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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Timeseries Sales Forecasting
In my current work sales forecasting and budgeting is being done rather classical way:
Take the sales from last year for comparable date and add or decrease X% on top to reflect recent trend.
This ...
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Is it possible to use leftovers data (warehouse stocks data) to create sales forecasting?
For example, i have sales data by categories
| Date | GE | VIC |
| -- | -- | --|
|03.01.2022 |2|7|
|10.01.2022 |30 |12|
|17.01.2022 |15 |5|
|24.01.2022 |57 |8|
|.....|...|...|
|28.08.2023 |16 |2|
And ...
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Model for small sample time series
Im a total novice and i need to estimate some kind of relation-proof model(Granger test results, correl matrix are already provide some evidence) with following dataset:
20 observations (2001-2021), 4 ...
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Supervised or Unsupervised Learning Classification: Facebook Prophet vs. ARIMA
I'm currently exploring time series forecasting and considering the use of Facebook's Prophet and ARIMA models. I'm a bit confused about whether these approaches fall under supervised or unsupervised ...
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Predicting quanting sold using Time series data
I am struggling with a time series dataset comprising 12 features, including quantity sold and weather data, totaling approximately 1800 values. My goal has been to forecast future values, quantity ...
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Is Kernel Density Estimation actually the continuous/curved version of a histogram?
I have been studying ARIMA for a bit of time now, and stumbled upon the plot_diagnostics function.
Among others, it plots out the Histogram & KDE on the same plot. As I did not know anything about ...
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How to predict in a forecasting model when the data after training and prediction is missing?
Let's say we have a forecasting model that was trained on any data before 2021 and now we need to make a prediction on data in 2023, for an accurate prediction we need to either give the data of 2022 ...
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Interpretting ar coefficient in arma proccess
I'm currently studying autocorrelation in Python and exploring the ArmaProcess module within the statsmodels.tsa.arima_process ...
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Time Series: ARIMA vs Random Forest Regressor
I have two model prediction results:
Using ARIMA model
Using Machine learning model where I used Random Forest Regressor
How do we compare these two? Is conventional time series modelling better or,...
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How to pass time series data to SARIMA, ARIMA, SARIMAX, etc
I am trying to predict stock price of a company, the data is non stationary.
Steps I followed -
Analyze the raw data
Determine whether the raw time series data is stationary or not using ADF and KPSS
...
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How to re-translate a forecast I wrote in R run on a dataset I downloaded from Tableau back into a SCRIPT_REAL function in Tableau
I want to add a SARIMA forecast of the next two days onto each line in the following Tableau graph:
But Tableau only does Exponential Smoothing forecasts and if I create a moving average table ...
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Remove Seasonality before applying SARIMA model on weekly data?
I am trying to predict average weekly stock prices for time series data.
Steps I followed:
I tested the data to check whether it was stationary or not using ADF ...
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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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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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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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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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